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Abstract:

Methods and systems are discussed for determining venous oxygen
saturation by calculating a ratio of ratios from respiration-induced
baseline modulations. A calculated venous ratio of ratios may be compared
with a look-up table value to estimate venous oxygen saturation. A
calculated venous ratio of ratios is compared with an arterial ratio of
ratios to determine whether baseline modulations are the result of a
subject's respiration or movement. Such a determination is also made by
deriving a venous ratio of ratios using a transform technique, such as a
continuous wavelet transform. Derived venous and arterial saturation
values are used to non-invasively determine a cardiac output of the
subject.

Claims:

1. A method for determining a subject's physiological condition,
comprising: obtaining a first photoplethysmographic signal from the
subject, based on light transmission at a first wavelength, and removing
a pulsatile component from the first signal to create a first filtered
signal indicative of a first baseline modulation; obtaining a second
photoplethysmographic signal from the subject, based on light
transmission at a second wavelength, and removing a pulsatile component
from the second signal to create a second filtered signal indicative of a
second baseline modulation; determining a first ratio that includes an
amplitude of the first filtered signal divided by a first numeric
component; determining a second ratio that includes an amplitude of the
second filtered signal divided by a second numeric component; and
dividing the first ratio by the second ratio to create a ratio of ratios
indicative of the subject's physiological condition.

2. The method of claim 1, wherein the first numeric component is a
modified amplitude of the first baseline modulation.

3. The method of claim 2, wherein the second numeric component is a
modified amplitude of the second baseline modulation.

4. The method of claim 3, wherein the first numeric component is a mean
baseline value of the first baseline modulation, and the second numeric
component is a mean baseline value of the second baseline modulation.

5. The method of claim 4, including the step of calculating a first
logarithm of the first ratio and a second logarithm of the second ratio.

6. The method of claim 5, wherein the step of dividing the first ratio by
the second ratio is performed by dividing the first logarithm by the
second logarithm to create the ratio of ratios.

7. The method of claim 1, further comprising determining venous oxygen
saturation based on the ratio of ratios.

8. The method of claim 7, wherein the step of determining venous oxygen
saturation includes comparing the ratio of ratios to a value in a look-up
table stored in memory on a processor.

9. The method of claim 8, wherein the look-up table comprises a set of
venous oxygen saturation values, wherein each value in the set of venous
oxygen saturation values is associated to a corresponding value of the
physiological condition.

10. The method of claim 7, wherein the step of determining venous oxygen
saturation includes mapping the ratio of ratios to venous oxygen
saturation values, wherein the mapping is derived empirically.

11. The method of claim 1, wherein the filtering is coordinated with a
parameter of a medical device.

12. The method of claim 11, wherein the parameter of the medical device
is a respiration rate of a ventilator.

16. The method of claim 1, wherein the first photoplethysmographic signal
and the second photoplethysmographic signal are obtained non-invasively.

17. A system for deriving information pertaining to physiological
information, the system comprising: a red light source and an infrared
light source, each configured to direct light onto the subject; a
detector that detects light from the light sources to provide a pulse
oximetry signal having pulsatile components indicative of light
transmission by arterial blood in the subject, and baseline components
indicative of light transmission by venous blood in the subject; a filter
that removes a subset of the pulsatile components from the pulse oximetry
signal to create a filtered signal; a signal processor programmed to: (a)
identify within the filtered signal a first amplitude indicative of a
baseline component from the red light source and a second amplitude
indicative of a baseline component from the infrared light source; (b)
determine a first ratio, comprising the first amplitude divided by a mean
of a plurality of amplitudes from the red light source; (c) determine a
second ratio, comprising the second amplitude divided by a mean of a
plurality of amplitudes from the infrared light source; and (d) divide
the first and second ratios to create a modified signal, indicative of
oxygen concentration in venous blood.

18. The system of claim 17, wherein the pulse oximetry signal is a
photoplethysmographic signal.

19. The system of claim 18, wherein the signal processor is configured to
determine venous oxygen saturation based on the modified signal.

20. The system of claim 19, wherein the venous oxygen saturation
determination is based on a look-up table.

21. The system of claim 20, wherein the look-up table comprises a set of
venous oxygen saturation values, wherein each value in a set of venous
oxygen saturation values is associated to a corresponding value of the
physiological information.

22. The system of claim 19, wherein the venous oxygen saturation
determination is based on a mapping of the ratio of ratios to venous
oxygen saturation values, wherein the mapping is derived empirically.

23. The system of claim 19, wherein the filter removes the pulsatile
components by filtering around a parameter of a medical device.

24. The system of claim 20, wherein the parameter of the medical device
is a respiration rate of a ventilator.

25. The system of claim 19, wherein the signal processor is further
configured to determine arterial oxygen saturation simultaneously using a
ratio-of-ratios type calculation involving the filtered pulsatile
components.

26. The system of claim 17, wherein a venous component represents
modulation of light transmission corresponding to venous blood in the
subject.

27. The system of claim 17, wherein the pulse oximetry signal is obtained
non-invasively.

28. The system of claim 17, wherein the signal processor is further
configured to extract the venous component and the baseline components
indicative of light transmission by venous blood in the subject by
filtering the pulse oximetry signal.

Description:

SUMMARY

[0001] The present disclosure relates to signal processing and analysis
and, more particularly, the present disclosure relates to systems and
methods for calculating and utilizing values related to venous oxygen
saturation.

[0002] In conventional pulse oximetry, a subject's arterial oxygen
saturation is estimated from a ratio of ratios calculated from the
amplitude of cardiac pulsatile components of red and infrared signals. In
the present disclosure, methods and systems are provided for estimating a
subject's venous oxygen saturation by calculating a ratio of ratios from
the amplitude of respiratory modulations that have been obtained from the
subject's pulse oximetry signal, such as photoplethysmographic ("PPG")
signal obtained from one or more sensing devices. Methods and systems are
also provided for using the ratio of ratios based on respiration
modulations to analyze the quality of the subject's PPG signal and to
identify features of the subject's physiological condition, such as
venous blood oxygen saturation and cardiac output.

[0003] In certain aspects, a ratio of ratios calculated based on
respiration modulations is compared to a calculated ratio of ratios based
on cardiac pulsatile components to determine signal quality. For example,
a signal quality metric may indicate the extent to which motion artifact
may be interfering with the detection of respiratory modulations or
cardiac pulses. This signal quality metric may also be used to determine
a confidence level for calculated arterial or venous oxygen saturation
values. A signal quality metric may also be calculated by computing the
wavelet transform of a physiological signal and examining one or more
regions of interest on a ratio surface derived from the transform. Based
in part on these signal quality metrics, signal processing algorithms may
adjust their function to compensate for motion artifact, appropriately
weight any calculated values, decide that it is not possible to calculate
sufficiently accurate values, activate an alarm, or take any other
appropriate action.

[0004] The present disclosure provides methods and systems for calculating
a ratio of ratios from respiratory modulation signals. The calculated
ratio of ratios may be compared with values in a look-up table to derive
a venous oxygen saturation value. The calculated ratio of ratios may
alternatively be mapped to venous oxygen saturation values. Such a
mapping may be derived empirically. Such estimates of venous oxygen
saturation are particularly relevant to subjects who use ventilators.
Because estimating venous oxygen saturation, unlike estimating arterial
oxygen saturation, does not require obtaining a physiological signal from
a part of the body, such as a finger or toe, with a strong cardiac
pulsatile component, alternative sites for obtaining physiological
signals may be used, such as a subject's chest wall or deeper regions of
the body. Once a venous oxygen saturation value is estimated from a
signal of sufficient quality, the venous oxygen saturation value may be
used with a derived arterial oxygen saturation value to determine a
patient's cardiac output non-invasively using Fick's equation or any
other applicable method.

[0005] In certain embodiments, methods are provided for determining a
subject's physiological condition by obtaining a first PPG signal from
the subject, based on light transmission at a first wavelength, and using
that signal to determine data indicative of the oxygen saturation of the
subject's blood. In particular, a pulsatile component is removed from the
first signal to create a first filtered signal indicative of a first
baseline modulation. A second PPG signal is also obtained from the
subject, based on light transmission at a second wavelength, and a
pulsatile component is removed from the second signal to create a second
filtered signal indicative of a second baseline modulation. A first ratio
is determined by dividing an amplitude of the first filtered signal by a
first numeric component. A second ratio is determined by dividing an
amplitude of the second filtered signal by a second numeric component.
The first ratio is divided by the second ratio to create a ratio of
ratios indicative of the subject's physiological condition.

[0006] In some embodiments, the first numeric component is a modified
amplitude of the first baseline modulation. In some embodiments, the
second numeric component is a modified amplitude of the second baseline
modulation. In some embodiments, the first numeric component is a mean
baseline value of the first baseline modulation, and the second numeric
component is a mean baseline value of the second baseline modulation. A
first logarithm of the first ratio and a second logarithm of the second
ratio may be calculated. In certain embodiments, the step of dividing the
first ratio by the second ratio is performed by dividing the first
logarithm by the second logarithm to create the ratio of ratios.

[0007] A venous oxygen saturation may be determined based on the ratio of
ratios. In some embodiments, determining venous oxygen saturation
includes comparing the ratio of ratios to a value in a look-up table. The
look-up table may include a set of venous oxygen saturation values, each
value in the set of venous oxygen saturation values being associated with
a corresponding value of the physiological venous oxygen saturation. In
some embodiments, determining venous oxygen saturation includes mapping
the ratio of ratios to venous oxygen saturation values. Such a mapping
may be derived empirically.

[0008] In some embodiments, the filtering is coordinated with a
respiration rate of a ventilator. In some embodiments, arterial oxygen
saturation is determined simultaneously with the venous oxygen saturation
using the removed pulsatile components. The arterial oxygen saturation
may be determined using

where βo and βr are empirically derived absorption
coefficients, λR and λIR are wavelengths, R is the
ratio of ratios, and s is the arterial oxygen saturation.

[0009] In some embodiments, the first PPG signal and the second PPG signal
are obtained non-invasively.

[0010] Systems are also provided for deriving the subject's venous oxygen
saturation or other physiological information, the subject's pulse
oximetry signal, which has pulsatile components indicative of light
transmission by arterial blood in the subject, and baseline components
indicative of light transmission by venous blood in the subject. The
systems include a filter that removes the pulsatile components from the
pulse oximetry signal to create a filtered signal and a signal processor
programmed to identify within the filtered signal a first amplitude
indicative of a baseline component from a red light source and a second
amplitude indicative of a baseline component from an infrared light
source. The signal processor is programmed to determine a first ratio
that includes the first amplitude divided by a mean of a plurality of
amplitudes from the red light source and a second ratio that includes the
second amplitude divided by a mean of a plurality of amplitudes from the
infrared light source. The signal processor is also programmed to divide
the first and second ratios to create a modified signal. In some
embodiments, the signal processor is further configured to determine
venous oxygen saturation based on the modified signal.

[0011] In some embodiments, the filter removes the pulsatile components by
filtering around a respiration rate of a ventilator. In some embodiments,
the signal processor is further configured to determine arterial oxygen
saturation simultaneously using ratio-of-ratios calculation involving the
filtered pulsatile components. In some embodiments, a venous component
represents modulation of light transmission corresponding to venous blood
in the subject. In some embodiments, the pulse oximetry signal is
obtained non-invasively. In some embodiments, the signal processor is
further configured to extract the venous component and the baseline
components indicative of light transmission by venous blood in the
subject by filtering the pulse oximetry signal.

[0012] Methods and systems are also provided for using the ratio of
ratios, corresponding to venous blood, to perform one or more analyses on
the signal to assess the source and quality of the signal. For example, a
calculated ratio of ratios based on respiration modulations may be used
to determine the extent to which motion is interfering with the detection
of respiratory modulations and the confidence in any calculated values. A
ratio of ratios of unity may be an indication of movement artifact. Also,
if an obtained physiological signal includes both a cardiac pulsatile
component and a secondary modulation component, a calculated ratio of
ratios based on the secondary modulation component that is similar to a
calculated ratio of ratios based on the cardiac pulse component may be a
positive indication that the secondary modulations are due to
respiration. The methods include, for example, calculating, from the
obtained physiological signal, a first ratio value indicative of a
secondary modulation in the obtained physiological signal and obtaining a
second ratio value indicative of a pulsatile component in the obtained
physiological signal. In certain implementations, if the first ratio
value and the second ratio value are very similar and neither are near
unity, this may indicate that the secondary modulations in the signal are
more likely caused by respiration than movement.

[0013] In some embodiments, systems are provided for analyzing a
physiological signal obtained from a subject, which include a signal
input configured to receive the physiological signal of the subject from
a sensing device. The systems also include one or more processing devices
in communication with the signal input and configured to calculate, from
the physiological signal, a first ratio value indicative of a respiration
modulation in the physiological signal. The one or more processing
devices are configured to calculate, from the physiological signal, a
second ratio value indicative of a pulsatile component in the
physiological signal. The one or more processing devices are also
configured to provide an indication of the first ratio value relative to
the second ratio value, which may be used to determine at least one of
the quality of the obtained signal and whether modulations in the signal
are due to respiration or movement.

[0014] In some embodiments, the indication of the first ratio value
relative to the second ratio value includes an indication of a difference
between a threshold value and a combined ratio of ratios, which may
indicate whether modulations in the signal are due to respiration or
motion of the subject. The combined ratio of ratios includes a function
of the first ratio of ratios and the second ratio of ratios. In some
embodiments, the threshold value is derived from a long-term difference
between respiration and pulsatile modulations in data collected from the
subject over time, which indicates oxygen demand at a part of the
subject's body (e.g. finger tip).

[0015] In some embodiments, the systems include an indicator for
indicating whether baseline modulation in a signal component is due to
respiration or motion of the subject. The indicator may indicate that a
baseline modulation in at least one of first and second wavelength
components taken from the subject is due to respiration of the subject
when there are small deviations of the combined ratio of ratios from a
threshold value. The indicator may indicate that a baseline modulation in
at least one of the first and second wavelength components is due to
motion of the subject when there are large deviations of the combined
ratio of ratios from the threshold value. The indicator may include an
alarm that is triggered when a baseline modulation in at least one of the
first and second wavelength components is due to motion of the subject.

[0016] In certain implementations, the signal quality of an obtained
physiological signal may be tested by transforming physiological signals.
In some embodiments, methods are provided that include transforming a
first physiological signal based on light transmission at a first
wavelength to generate a first transformed signal. The methods include
transforming a second physiological signal based on light transmission at
a second wavelength to generate a second transformed signal. A ratio
surface is derived from the first transformed signal and the second
transformed signal, and a first region of interest on the ratio surface
indicative of venous perturbation is identified, which may be related to
a respiration rate of the subject. A representative value is calculated
for the first region of interest on the ratio surface. Based on the
calculated representative value, the quality of the signals may be
evaluated by determining whether the representative value for the first
region of interest indicates respiration or motion of the subject.

[0017] In some embodiments, deriving the ratio surface involves
normalizing the first and second physiological signals by a value, for
example dividing the respective magnitude of each of the first and second
physiological signals by the respective minimum, maximum, mean, DC
component, or standard deviation computed over a time window of the first
and second physiological signals.

[0018] In some embodiments, transforming the first and second signal
includes using a wavelet transform. In some embodiments, the wavelet
transform is applied to derivatives of the first and second signals.

[0019] In some embodiments, determining whether the representative value
for the first region of interest indicates respiration or motion of the
subject involves identifying a second region of interest on the ratio
surface related to a cardiac pulse frequency. A representative value is
calculated for the second region of interest, and the representative
value for the first region of interest is compared with the
representative value for the second region of interest. The
representative values for the first and second regions of interest may
correspond to respective first and second functions. Comparing the
representative value for the first region of interest with the
representative value for the second region of interest may include, for
example, comparing corresponding points on the first and second
functions, respective median values of the first and second functions,
respective average values of the first and second functions, or
corresponding portions of the first and second functions. Similar
representative values for the first and second regions of interest that
are not near unity are indicative of baseline modulations in the first
and second signals being more likely caused by respiration than movement.

[0020] In some embodiments, systems provide one or more processing devices
that transform a first physiological signal based on light transmission
at a first wavelength to generate a first transformed signal. The one or
more processing devices may also be configured to transform a second
physiological signal based on light transmission at a second wavelength
to generate a second transformed signal. One or more processing devices
are configured to derive a ratio surface from the first transformed
signal and the second transformed signal and to calculate a
representative value for a first region of interest on the ratio surface,
which may be related to a respiration rate of the subject. The calculated
representative value may indicate whether baseline modulation in at least
one of the first and second signals is due to respiration of the subject.

[0021] In some embodiments, the one or more processing devices are
configured to transform the first and second signal using a wavelet
transform. In some embodiments, the one or more processing devices are
configured to calculate a first modulus of the transform of the first
signal, calculate a second modulus of the transform of the second signal,
and divide the first modulus by the second modulus, resulting in the
ratio surface from which representative values indicative of signal
quality can be derived.

[0022] Methods and systems are also provided for using venous oxygen
saturation values to non-invasively assess physiological conditions of
the subject. Such non-invasive methods and systems provide several
advantages over invasive techniques, including minimizing the subject's
pain and recovery time. In some embodiments, non-invasive methods are
provided for determining cardiac information about a subject by obtaining
a first non-invasive physiological signal that includes a component
indicative of arterial blood in the subject and a second non-invasive
physiological signal that includes a component indicative of venous blood
in the subject. The venous blood may be mixed venous blood, central
venous blood, or other venous blood of interest in the methods described
herein. An arterial blood oxygen content is determined from the first
physiological signal and a venous blood oxygen content is determined from
the second physiological signal. A cardiac output is determined, for
example by using Fick's equation, based at least in part on the arterial
blood oxygen content and the venous blood oxygen content.

[0023] In some embodiments, the first physiological signal and second
physiological signal are PPG signals. In some embodiments, an oxygen
consumption rate of the subject is measured and used with the arterial
and venous blood contents to calculate the cardiac output. In some
embodiments, determining the cardiac output includes determining the
amount of oxygen consumed by the patient, determining an arterio-venous
oxygen concentration difference, and determining cardiac output as a flow
rate by dividing the oxygen consumption by the concentration difference.

[0024] Computer readable media are also provided for non-invasively
determining venous oxygen saturation, assessing signal quality using the
respiratory modulations, and assessing physiological conditions of the
subject. In some embodiments, computer readable media have stored
instructions that when executed direct a first input port to receive a
first non-invasive physiological signal that includes a component
indicative of arterial blood, and direct a second input port to receive a
second non-invasive physiological signal that includes a component
indicative of venous blood return. The computer readable media direct
processing equipment to determine an arterial blood oxygen content based
at least in part on a first set of components derived from the first
physiological signal and to determine a venous blood oxygen content based
at least in part on a second set of components derived from the second
physiological signal. The computer readable media also direct processing
equipment to determine, for example using Fick's equation, a cardiac
output based at least in part on the oxygen consumption rate, the
arterial blood oxygen content, and the venous blood oxygen content.

[0025] In some embodiments, the computer readable media direct a third
input port to receive a signal that measures an oxygen consumption rate
of the subject, which can then be used with the arterial and venous blood
contents to calculate the cardiac output. In some embodiments, processing
equipment is directed to determine an arterio-venous oxygen concentration
difference by subtracting the venous blood oxygen content from the
arterial blood oxygen content, and to determine cardiac output as a flow
rate by dividing the oxygen consumption rate by the concentration
difference.

BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and other features of the present disclosure, its nature
and various advantages will be more apparent upon consideration of the
following detailed description, taken in conjunction with the
accompanying drawings in which:

[0027] FIG. 1 shows an illustrative pulse oximetry system in accordance
with some embodiments;

[0028]FIG. 2 is a block diagram of the illustrative pulse oximetry system
of FIG. 1 coupled to a patient in accordance with some embodiments;

[0029]FIG. 3 is a block diagram of an illustrative signal processing
system in accordance with some embodiments;

[0030] FIG. 4(a) shows an illustrative PPG signal in accordance with some
embodiments;

[0037] FIG. 7(b) shows an illustrative ratio signal obtained by dividing
the red and infrared signals of FIG. 7(a) by each other in accordance
with some embodiments;

[0038] FIG. 7(c) shows an illustrative filtered ratio signal of the
illustrative ratio signal of FIG. 7(b) in accordance with some
embodiments;

[0039] FIG. 8 shows an illustrative mean of the 40th to 60th
percentile range of values of the illustrative ratio signal of FIG. 7(b)
in accordance with some embodiments;

[0040]FIG. 9 is a flow chart of illustrative steps for analyzing a
physiological signal obtained from a subject in accordance with some
embodiments;

[0041] FIG. 10 is a flow chart of illustrative steps for analyzing a
physiological signal obtained from a subject in accordance with some
embodiments;

[0042]FIG. 11 is a flow chart of illustrative steps for analyzing a
physiological signal obtained from a subject in accordance with some
embodiments;

[0043] FIGS. 12(a) and 12(b) show illustrative views of a scalogram
derived from a PPG signal in accordance with some embodiments;

[0044] FIG. 12(c) shows an illustrative scalogram derived from a signal
containing two pertinent components in accordance with some embodiments;

[0045] FIG. 12(d) shows an illustrative schematic of signals associated
with FIG. 12(c) and further wavelet decomposition thereof in accordance
with some embodiments;

[0046] FIGS. 12(e) and 12(f) are flow charts of illustrative steps
involved in performing an inverse continuous wavelet transform in
accordance with some embodiments;

[0047]FIG. 13 shows an illustrative wavelet transform ratio surface of
the normalized respiration modulation signals of FIG. 7(a) in accordance
with some embodiments;

[0048] FIG. 14 is a flow chart of illustrative steps for analyzing a
respiration modulation signal obtained from a subject in accordance with
some embodiments;

[0049]FIG. 15 shows an illustrative representative value of the
illustrative ratio surface of FIG. 13 in accordance with some
embodiments;

[0050]FIG. 16 is a flow chart of illustrative steps for analyzing a
physiological signal obtained from a subject in accordance with some
embodiments;

[0051] FIG. 17 is a flow chart of illustrative steps for non-invasively
determining a cardiac output in accordance with some embodiments;

[0052] FIG. 18 is a flow chart of illustrative steps for non-invasively
determining a cardiac output using a first measured physiological signal
and a second measured physiological signal in accordance with some
embodiments; and

[0053] FIG. 19 is a flow chart of illustrative steps for non-invasively
determining a cardiac output and correcting for dissolved gases in
accordance with some embodiments.

DETAILED DESCRIPTION

[0054] An oximeter is a medical device that is commonly used to determine
the oxygen saturation of a patient's blood. One common type of oximeter
is a pulse oximeter, which indirectly measures the oxygen saturation of a
patient's blood (as opposed to measuring oxygen saturation directly by
analyzing a blood sample taken from the patient) and changes in blood
volume in the skin. Ancillary to the blood oxygen saturation measurement,
pulse oximeters are also used to measure the pulse rate of the patient.
Pulse oximeters typically measure and display various blood flow
characteristics including, but not limited to, the oxygen saturation of
hemoglobin in arterial blood.

[0055] An oximeter is typically used with a light sensor that is placed at
a site on a patient, typically a fingertip, toe, forehead or earlobe, or
in the case of a neonate, across a foot. The oximeter passes light using
a light source through blood perfused tissue and photoelectrically senses
the absorption of light in the tissue. For example, the oximeter may
measure the intensity of light that is received at the light sensor as a
function of time. A signal representing light intensity versus time or a
mathematical manipulation of this signal (e.g., a scaled version thereof,
a log taken thereof, a scaled version of a log taken thereof, etc.) may
be referred to as the PPG signal. In addition, the term "PPG signal," as
used herein, may also refer to an absorption signal (i.e., representing
the amount of light absorbed by the tissue) or any suitable mathematical
manipulation thereof. The light intensity or the amount of light absorbed
may then be used to calculate the amount of the blood constituent (e.g.,
oxyhemoglobin) being measured as well as the pulse rate and when each
individual pulse occurs.

[0056] The light passed through the tissue is selected to be of one or
more wavelengths that are absorbed by the blood in an amount
representative of the amount of the blood constituent present in the
blood. The amount of light passed through the tissue varies in accordance
with the changing amount of blood constituent in the tissue and the
related light absorption. Red and infrared (IR) wavelengths may be used
because it has been observed that highly oxygenated blood will absorb
relatively less red light and more infrared light than blood with a lower
oxygen saturation. By comparing the intensities of two wavelengths at
different points in the pulse cycle, it is possible to estimate the blood
oxygen saturation of hemoglobin in arterial blood.

[0057] When the measured blood parameter is the oxygen saturation of
hemoglobin, a convenient starting point assumes a saturation calculation
based on Lambert-Beer's law. The following notation will be used herein:

I=(λ,t)=IO(λ)exp(-(sβO(λ)+(1-s)β.s-
ub.r(λ))Cl(t)) (1)

where:

[0058] λ=wavelength;

[0059] t=time;

[0060] I=intensity of
light detected;

[0061] IO=intensity of light transmitted;

[0062]
s=oxygen saturation;

[0063] βO, βr=empirically
derived absorption coefficients; and

[0064] Cl(t)=a combination of
hemoglobin concentration and path length from emitter to detector as a
function of time.

[0065] The traditional approach measures light absorption at two
wavelengths (e.g., red and IR), and then calculates arterial blood oxygen
saturation by solving for a "ratio of ratios" as follows:

1. First, the natural logarithm of (1) is taken ("log" will be used to
represent the natural logarithm) for IR and red wavelengths

which defines a cluster of points whose slope of y versus x will give R
where

x(t)=[I(t2,λIR)-I(t1,IIR)]I(t1,λ.su-
b.R)

y(t)=[I(t2,λR)-(t1,λR)]I(t1,λ-
IR)

y(t)=Rx(t) (8)

[0068] FIG. 1 is an illustrative perspective view of a pulse oximetry
system 10 in accordance with some embodiments. System 10 includes a
sensor 12 and a pulse oximetry monitor 14. Sensor 12 includes an emitter
16 for emitting light at two or more wavelengths into a patient's tissue.
A detector 18 is also provided in sensor 12 for detecting the light
originally from emitter 16 that emanates from the patient's tissue after
passing through the tissue.

[0069] In some embodiments and as will be further described in relation to
FIG. 2, system 10 includes a plurality of sensors forming a sensor array
in lieu of single sensor 12. Each of the sensors of the sensor array may
be a complementary metal oxide semiconductor (CMOS) sensor, photodiode,
phototransistor, or charged coupled device (CCD) sensor, individually or
in various combinations. In some embodiments, the sensor array is made up
of a combination of CMOS and CCD sensors. The CCD sensor may comprise a
photoactive region and a transmission region for receiving and
transmitting data whereas the CMOS sensor may be made up of an integrated
circuit having an array of pixel sensors. Each pixel may have a
photodetector and an active amplifier.

[0070] In some embodiments, emitter 16 and detector 18 are on opposite
sides of a digit such as a finger or toe, in which case the light that is
emanating from the tissue has passed completely through the digit. In
some embodiments, emitter 16 and detector 18 are arranged so that light
from emitter 16 penetrates the tissue and is reflected by the tissue into
detector 18, such as a sensor designed to obtain pulse oximetry data from
a patient's forehead.

[0071] In some embodiments, the sensor or sensor array is connected to and
draws its power from monitor 14 as shown. In some embodiments, the sensor
is wirelessly connected to monitor 14 and includes its own battery or
similar power supply (not shown). Monitor 14 may be configured to
calculate physiological parameters based at least in part on data
received from sensor 12 relating to light emission and detection. In some
embodiments, the calculations are performed on the monitoring device
itself and the result of the oximetry reading is passed to monitor 14.
Further, monitor 14 may include a display 20 configured to display the
physiological parameters or other information about the system. In some
embodiments, monitor 14 also includes a speaker 22 to provide an audible
sound that may be used in various other embodiments, such as for example,
sounding an audible alarm in the event that a patient's physiological
parameters are not within a predefined normal range.

[0072] In some embodiments, sensor 12, or the sensor array, is
communicatively coupled to monitor 14 via a cable 24. In some
embodiments, a wireless transmission device (not shown) or the like is
used instead of or in addition to cable 24.

[0073] In some embodiments, pulse oximetry system 10 also includes a
multi-parameter patient monitor 26. The monitor may be a cathode ray tube
type, a flat panel display (as shown) such as a liquid crystal display
(LCD) or a plasma display, or any other type of monitor now known or
later developed. Multi-parameter patient monitor 26 may be configured to
calculate physiological parameters and to provide a display 28 for
information from monitor 14 and from other medical monitoring devices or
systems (not shown). For example, multi-parameter patient monitor 26 may
be configured to display an estimate of a patient's blood oxygen
saturation generated by pulse oximetry monitor 14 (referred to as an
"SpO2" measurement), pulse rate information from monitor 14 and
blood pressure from a blood pressure monitor (not shown) on display 28.

[0074] Monitor 14 may be communicatively coupled to multi-parameter
patient monitor 26 via a cable 32 or 34 that is coupled to a sensor input
port or a digital communications port, respectively and/or may
communicate wirelessly (not shown). In addition, monitor 14 and/or
multi-parameter patient monitor 26 may be coupled to a network to enable
the sharing of information with servers or other workstations (not
shown). Monitor 14 may be powered by a battery (not shown) or by a
conventional power source such as a wall outlet.

[0075]FIG. 2 is a block diagram of a pulse oximetry system, such as pulse
oximetry system 10 of FIG. 1, which is coupled to a patient 40 in
accordance with some embodiments. As used herein, a patient may be a
subject or any other entity from which physiological signals are
obtained. Certain illustrative components of sensor 12 and monitor 14 are
illustrated in FIG. 2. Sensor 12 includes emitter 16, detector 18, and
encoder 42. In some embodiments, emitter 16 is configured to emit at
least two wavelengths of light (e.g., red and IR) into a patient's tissue
40. Hence, emitter 16 may include a red light emitting light source such
as red light emitting diode (LED) 44 and an IR light emitting light
source such as IR LED 46 for emitting light into the patient's tissue 40
at the wavelengths used to calculate the patient's physiological
parameters. In some embodiments, the red wavelength is between about 600
nm and about 700 nm, and the IR wavelength is between about 800 nm and
about 1000 nm. In certain implementations where a sensor array is used in
place of single sensor, each sensor may be configured to emit a single
wavelength. For example, a first sensor emits only a red light while a
second only emits an IR light.

[0076] It will be understood that, as used herein, the term "light" may
refer to energy produced by radiative sources and may include one or more
of ultrasound, radio, microwave, millimeter wave, infrared, visible,
ultraviolet, gamma ray or X-ray electromagnetic radiation. As used
herein, light may also include any wavelength within the radio,
microwave, infrared, visible, ultraviolet, or X-ray spectra, and that any
suitable wavelength of electromagnetic radiation may be appropriate for
use with the present techniques. Detector 18 may be chosen to be
specifically sensitive to the chosen targeted energy spectrum of the
emitter 16.

[0077] In some embodiments, detector 18 is configured to detect the
intensity of light at the red and IR wavelengths. Alternatively, each
sensor in the array may be configured to detect an intensity of a single
wavelength. In operation, light enters detector 18 after passing through
the patient's tissue 40. Detector 18 converts the intensity of the
received light into an electrical signal. The light intensity is directly
related to the absorbance and/or reflectance of light in the patient's
tissue 40. That is, when more light at a certain wavelength is absorbed
or reflected, less light of that wavelength is received from the tissue
by the detector 18. After converting the received light to an electrical
signal, detector 18 sends the signal to monitor 14, where physiological
parameters are calculated based on the absorption of the red and IR
wavelengths in the patient's tissue 40.

[0078] In some embodiments, encoder 42 contains information about sensor
12, such as what type of sensor it is (e.g., whether the sensor is
intended for placement on a forehead or digit) and the wavelengths of
light emitted by emitter 16. This information may be used by monitor 14
to select appropriate algorithms, look-up tables and/or calibration
coefficients stored in monitor 14 for calculating the patient's
physiological parameters.

[0079] Encoder 42 may contain information specific to patient 40, such as,
for example, the patient's age, weight, and diagnosis. This information
may allow monitor 14 to determine, for example, patient-specific
threshold ranges in which the patient's physiological parameter
measurements should fall and to enable or disable additional
physiological parameter algorithms. Encoder 42 may, for instance, be a
coded resistor which stores values corresponding to the type of sensor 12
or the type of each sensor in the sensor array, the wavelengths of light
emitted by emitter 16 on each sensor of the sensor array, and/or the
patient's characteristics. In another embodiment, encoder 42 includes a
memory on which one or more of the following information may be stored
for communication to monitor 14: the type of the sensor 12, the
wavelengths of light emitted by emitter 16, the particular wavelength
each sensor in the sensor array is monitoring, a signal threshold for
each sensor in the sensor array, any other suitable information, or any
combination thereof.

[0080] In some embodiments, signals from detector 18 and encoder 42 are
transmitted to monitor 14. In some embodiments, monitor 14 includes a
general-purpose microprocessor 48 connected to an internal bus 50.
Microprocessor 48 may be adapted to execute software, which may include
an operating system and one or more applications, as part of performing
the functions described herein. Also connected to bus 50 may be a
read-only memory (ROM) 52, a random access memory (RAM) 54, user inputs
56, display 20, and speaker 22.

[0081] RAM 54 and ROM 52 are illustrated by way of example, and not
limitation. Any suitable computer-readable media may be used in the
system for data storage. Computer-readable media are capable of storing
information that can be interpreted by microprocessor 48. This
information may be data or may take the form of computer-executable
instructions, such as software applications, that cause the
microprocessor to perform certain functions and/or computer-implemented
methods. Depending on the embodiment, such computer-readable media may
include computer storage media and communication media. Computer storage
media may include volatile and non-volatile, removable and non-removable
media implemented in any method or technology for storage of information
such as computer-readable instructions, data structures, program modules
or other data. Computer storage media may include, but is not limited to,
RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory
technology, CD-ROM, DVD, or other optical storage, magnetic cassettes,
magnetic tape, magnetic disk storage or other magnetic storage devices,
or any other medium which can be used to store the desired information
and which can be accessed by components of the system.

[0082] In some embodiments, a time processing unit (TPU) 58 provides
timing control signals to a light drive circuitry 60, which controls when
emitter 16 is illuminated and multiplexed timing for the red LED 44 and
the IR LED 46. TPU 58 may also control the gating-in of signals from
detector 18 through an amplifier 62 and a switching circuit 64. These
signals are sampled at the proper time, depending upon which light source
is illuminated. The received signal from detector 18 may be passed
through an amplifier 66, a low pass filter 68, and an analog-to-digital
converter 70. The digital data may then be stored in a queued serial
module (QSM) 72 (or buffer) for later downloading to RAM 54 as QSM 72
fills up. In some embodiments, there are multiple separate parallel paths
having amplifier 66, filter 68, and A/D converter 70 for multiple light
wavelengths or spectra received.

[0083] In some embodiments, microprocessor 48 determines the patient's
physiological parameters, such as SpO2 and pulse rate, using various
algorithms and/or look-up tables based on the value of the received
signals and/or data corresponding to the light received by detector 18.
In some embodiments, microprocessor 48 is used for signal processing. For
example, microprocessor 48 may calculate an archetype transform using a
weighted averaging scheme. Signals corresponding to information about
patient 40, and particularly about the intensity of light emanating from
a patient's tissue over time, are transmitted from encoder 42 to a
decoder 74. These signals may include, for example, encoded information
relating to patient characteristics. Decoder 74 translates these signals
to enable the microprocessor to determine the thresholds based on
algorithms or look-up tables stored in ROM 52. User inputs 56 may be used
to enter information about the patient, such as age, weight, height,
diagnosis, medications, treatments, and so forth. In some embodiments,
display 20 exhibits a list of values which may generally apply to the
patient, such as, for example, age ranges or medication families, which
the user may select using user inputs 56.

[0084] The optical signal through the tissue can be degraded by noise,
among other sources. One source of noise is ambient light that reaches
the light detector. Another source of noise is electromagnetic coupling
from other electronic instruments. Movement of the patient also
introduces noise and affects the signal. For example, the contact between
the detector and the skin, or the emitter and the skin, can be
temporarily disrupted when movement causes either to move away from the
skin. In addition, because blood is a fluid, it responds differently than
the surrounding tissue to inertial effects, thus resulting in momentary
changes in volume at the point to which the oximeter probe is attached.

[0085] Noise (e.g., from patient movement) can degrade a pulse oximetry
signal relied upon by a physician, without the physician's awareness.
This is especially true if the monitoring of the patient is remote, the
motion is too small to be observed, or the doctor is watching the
instrument or other parts of the patient, and not the sensor site.
Processing pulse oximetry (i.e., PPG) signals may involve operations,
such as filtering, that reduce the amount of noise present in the signals
or otherwise identify noise components in order to prevent them from
affecting measurements of physiological parameters derived from the PPG
signals.

[0086] It will be understood that the present disclosure is applicable to
any suitable signals and that PPG signals are used merely for
illustrative purposes. Those skilled in the art will recognize that the
present disclosure has wide applicability to other signals including, but
not limited to other biosignals (e.g., electrocardiogram,
electroencephalogram, electrogastrogram, electromyogram, heart rate
signals, pathological sounds, ultrasound, or any other suitable
biosignal), dynamic signals, non-destructive testing signals, condition
monitoring signals, fluid signals, geophysical signals, astronomical
signals, electrical signals, financial signals including financial
indices, sound and speech signals, chemical signals, meteorological
signals including climate signals, and/or any other suitable signal,
and/or any combination thereof.

[0088] In some embodiments, signal 316 is coupled to processor 312.
Processor 312 may be any suitable software, firmware, and/or hardware,
and/or combinations thereof for processing signal 316. For example,
processor 312 may include one or more hardware processors (e.g.,
integrated circuits), one or more software modules, computer-readable
media such as memory, firmware, or any combination thereof. Processor 312
may, for example, be a computer or may be one or more chips (i.e.,
integrated circuits). Processor 312 may perform any suitable signal
processing of signal 316 to filter signal 316, such as any suitable
band-pass filtering, adaptive filtering, closed-loop filtering, and/or
any other suitable filtering, and/or any combination thereof.

[0089] Processor 312 may be coupled to one or more memory devices (not
shown) or incorporate one or more memory devices such as any suitable
volatile memory device (e.g., RAM, registers, etc.), non-volatile memory
device (e.g., ROM, EPROM, magnetic storage device, optical storage
device, flash memory, etc.), or both. The memory may be used by processor
312 to, for example, store threshold values and/or look-up table values,
as discussed further in relation to FIGS. 6 and 9.

[0090] Processor 312 is coupled to output 314. Output 314 may be any
suitable output device such as, for example, one or more medical devices
(e.g., a medical monitor that displays various physiological parameters,
a medical alarm, or any other suitable medical device that either
displays physiological parameters or uses the output of processor 312 as
an input), one or more display devices (e.g., monitor, PDA, mobile phone,
any other suitable display device, or any combination thereof), one or
more audio devices, one or more memory devices (e.g., hard disk drive,
flash memory, RAM, optical disk, any other suitable memory device, or any
combination thereof), one or more printing devices, any other suitable
output device, or any combination thereof.

[0091] It will be understood that system 300 may be incorporated into
system 10 (FIGS. 1 and 2) in which, for example, input signal generator
310 is implemented as part of sensor 12 and monitor 14, and the processor
312 is implemented as part of monitor 14.

[0093] PPG signal 402 includes two signal components as shown in FIG.
4(a), PPG signal 402 includes a pulsatile component 406 and a baseline
modulation component 404. Pulsatile component 406 may be attributed to
variations in the subject's blood flow that are caused by cardiac
activity. Baseline modulation component 404 is attributed to other
variations in the subject's blood flow that are caused by the subject's
respiration activity. In some instances, the subject's respiration
activity is influenced by or is due to the subject using a ventilator.
Baseline modulation component 404 may be indicative of the subject's
venous blood flow.

[0094] FIG. 4(b) shows a schematic 404 of the PPG signal of FIG. 4(a) that
has been filtered at or around a respiration rate (e.g., 0.25 Hz) to
remove the pulsatile component 406 and preserve the baseline modulation
component 404. Baseline modulation component 404 shows an illustration of
the signal obtained after filtering PPG signal 402 to remove pulsatile
component 406. Filtering techniques for obtaining baseline modulation
component 404 from PPG signal 402 are described in detail with respect to
the steps of FIG. 6 below. Baseline modulation component 404 includes
certain characteristics. For example, extremum 408, which corresponds to
a maximum of the baseline modulation component 404, may be a feature used
to characterize the baseline modulation component 404. Use of such
characteristics is explained further with respect to FIGS. 5(a) and 5(b)
below.

[0095] FIGS. 5(a) and 5(b) show illustrative schematics of a filtered red
PPG signal 506 and a filtered infrared PPG signal 518, respectively, in
accordance with some embodiments. The PPG signals may be taken from a
pulse oximeter probe placed, for example, on a subject's chest wall.
Filtered red PPG signal 506 is obtained by low-pass filtering a red PPG
signal to extract respiratory modulations. Y-axis 502 of FIG. 5(a)
denotes the amplitude of the respiratory modulations from the red PPG
signal 506. X-axis 504 of FIG. 5(a) denotes time, in seconds, increasing
from left to right. The line 528 represents the mean value over time
(i.e., baseline) of the respiratory modulations. The baseline 528 can be
computed, for example, by low-pass filtering the respiratory modulations
at a frequency lower than the respiration rate.

[0096] In some embodiments, time points 508 and 510 denote the beginning
and the end points of a time window 512 within which an extremum 526 is
identified. The duration of time window 512 is the difference between
time points 510 and 508. In some embodiments, time window 512 has
duration of 6 seconds. Time windows of longer or shorter time durations
than 6 seconds may also be used depending on the context or the subject's
condition.

[0097] FIG. 5(b) shows a filtered infrared PPG signal 518 in accordance
with some embodiments. Filtered red PPG signal 518 is obtained by
low-pass filtering an infrared PPG signal to extract the baseline
modulation component. Y-axis 514 of FIG. 5(b) denotes the amplitude of
filtered infrared PPG signal 518. X-axis 516 of FIG. 5(b) denotes time,
in seconds, increasing from left to right. In some embodiments, time
points 520 and 522 denote the beginning and the end points of a time
window 524 within which an extremum 530 may be identified. The duration
of time window 524 is the difference between time points 520 and 522. In
some embodiments, time window 524 has a duration of 6 seconds. Time
windows of longer or shorter time durations than 6 seconds may also be
used depending on the context or the subject's condition.

[0098] Depending on which physiological condition of the subject is being
determined, either the pulsatile component 406 or the baseline modulation
component 404, or both components, may be utilized. For example, in some
embodiments, pulsatile component 406 is utilized for determining the
subject's arterial oxygen saturation. In some embodiments, sites on a
subject's body conventionally used for oximetry (e.g., finger, forehead
or ear) are used to obtain the red and the infrared PPG signals used for
determining the subject's arterial oxygen saturation.

Respiration Modulation

[0099] The baseline modulation component 404 may be utilized for
determining the subject's venous oxygen saturation, as discussed in
relation to FIG. 6. In some embodiments, because determining the
subject's venous oxygen saturation does not require a cardiac pulsatile
component, alternative sites on a subject's body not conventionally used
for oximetry are used to obtain the red and the infrared PPG signals. For
example, optical or other suitable techniques may be used to obtain the
red and the infrared PPG signals from deeper regions in a subject's body
for use in determining the subject's venous oxygen saturation. Such
techniques provide information from deeper or more central parts of the
subject's body and may permit more accurate determinations of the
subject's venous oxygen saturation.

[0100] In some embodiments, the arterial oxygen saturation and the venous
oxygen saturation, determined using pulsatile component 406 and baseline
modulation component 404, respectively, are used for determining the
subject's cardiac output. The use of the saturation values for
determining cardiac output is described with respect to FIGS. 17-19.

[0101] Determination of venous oxygen saturation is discussed with respect
to FIG. 6. FIG. 6 illustrates how to obtain a signal (step 602) and then
how to process the signal to obtain a venous oxygen saturation value
(steps 604, 606, 608, 610, and 612). The steps of flow chart 600 may be
performed by processing equipment such as processor 316 of FIG. 3,
microprocessor 48 of FIG. 2, or any suitable processing device. The steps
of flow chart 600 may be performed by a digital processing device, or
implemented in analog hardware. It will be noted that the steps of flow
chart 600 may be performed in any suitable order, and one or more steps
may be omitted entirely according to the context and application.

[0102] At step 602, a plurality of signals is obtained. A signal (e.g., a
PPG signal) may be obtained from any suitable source (e.g., sensor 12 of
FIG. 2) using any suitable technique. A sensor from which a signal is
obtained may include any of the physiological sensors described herein,
or any other sensor. An obtained signal may be signal 402 as shown in
FIG. 4(a). An obtained signal may include multiple signals, for example,
in the form of a multi-dimensional vector signal or a
frequency-multiplexed or time-multiplexed signal. In some embodiments,
the plurality of signals obtained at step 602 include two or more PPG
signals, which may be measured at two or more respective body sites of a
subject.

[0103] The plurality of signals obtained at step 602 include first and
second physiological signals. In some embodiments, a first signal is a
PPG signal corresponding to a red wavelength, and a second signal is a
PPG signal corresponding to an infrared wavelength. The red and infrared
wavelengths may correspond to those used in traditional pulse oximetry,
or entirely different wavelengths may be used. In some embodiments, each
of the first and second signals obtained at step 602 includes a cardiac
pulsatile component and a baseline modulation component, such as
pulsatile component 406 of FIG. 4(a) and baseline modulation component
404 of FIG. 4(b). In some embodiments, first and second signals are
obtained by first and second sensors located at approximately the same
body site of a subject. In some embodiments, first and second signals are
obtained by first and second sensors located at different body sites of a
subject. For example, first and second signals included in a plurality of
signals may be electronic signals from pulse oximetry sensors located at
two different body sites of a subject. It will be noted that the steps of
flow diagram 600 may be applied to any number of obtained signals in
accordance with the techniques described herein.

[0104] At step 604, one or more of the plurality of signals obtained at
step 602 is processed to remove pulsatile components such as pulsatile
component 406 and generate a corresponding respiratory modulation signal.
The processing may occur when the signal is acquired in step 602 or as a
subsequent processing step. A processing operation may be performed by
any suitable processing device, such as processor 312 (FIG. 3), which may
be a general-purpose computing device or a specialized processor. A
processing operation may be performed by a separate, dedicated device, or
by a series of devices (e.g., an analog filter and a programmed
microprocessor). Any of the processing steps described herein may be used
to remove the pulsatile component from the plurality of signals obtained
at step 602.

[0105] A processing operation may transform the original and/or
transformed signals into any suitable domain. In some embodiments, the
processing at step 604 includes transforming a signal into another
domain, for example, a Fourier, wavelet, spectral, scale, time,
time-spectral, or time-scale domain, or any transform space. Wavelet
transforms are further discussed below with respect to FIGS. 12(a)-(f).

[0106] The processing at step 604 may include filtering a signal or
mathematically manipulating one or multiple signals. For example, a
processed signal may be based at least in part on past values of a
signal, such as signal 316 (FIG. 3), which may be retrieved by processor
312 (FIG. 3) from a memory such as a buffer memory or RAM 54 (FIG. 2).
Many examples of processing operations are discussed in detail herein,
but it will be understood that the techniques of the present disclosure
are not limited to these examples.

[0107] The processing operations of step 604 may include any one or more
of the following: compressing, multiplexing, modulating, up-sampling,
down-sampling, smoothing, taking a median or other statistic of the
obtained signal, removing erroneous regions of the obtained signal, or
any combination thereof. In some embodiments, a normalization step is
performed which divides the magnitude of a signal obtained at step 602 by
a value. This value may be based on at least one of the maximum of the
obtained signal, the minimum of the obtained signal and the mean of the
obtained signal. In some embodiments, a signal obtained at step 602 is
normalized by dividing the signal by a DC component. In some embodiments,
a signal obtained at step 602 is normalized by dividing the signal by the
standard deviation of the signal computed over a time window. In some
embodiments, the processing operations at step 604 include one or more
mathematical manipulations. Mathematical manipulations may include any
linear or non-linear combination or signals or portions of signals, and
may be performed in any suitable domain (e.g., time, frequency and
wavelet domains).

[0108] In some embodiments, the processing operations at step 604 include
one or more time derivatives. A time derivative may be calculated by
processor 312 (FIG. 3). A time derivative may be calculated by any of a
number of derivative/gradient determination and approximation techniques,
including those suitable for sampled data (e.g., forward difference,
backward difference, central difference, higher-order methods, and any
automated numerical or symbolic differentiation method).

[0109] In some embodiments, the processing operations at step 604 include
filtering using any suitable filtering technique. For example, a signal
received at sensor unit 12 (FIGS. 1 and 2) may be filtered at step 604 by
low pass filter 68 (FIG. 2) prior to undergoing additional processing at
microprocessor 48 (FIG. 2) within patient monitoring system 10 (FIGS. 1
and 2). Low-pass filter 68 (FIG. 2) may selectively remove frequencies
that may later be ignored by further processing or analysis steps, which
may advantageously reduce computational time and memory requirements. In
some embodiments, one or more signals obtained at step 602 are low- or
band-pass filtered at step 604 to remove high frequencies. In some
embodiments, one or more signals obtained at step 602 are filtered at
step 604 to remove a DC component. In some embodiments, an obtained PPG
signal is low-pass filtered at step 604 to pass frequencies in the
approximate range 0-0.25 Hz to remove non-respiratory frequencies. In
some embodiments, an obtained PPG signal is band-pass filtered at step
604 to pass selected frequencies. In some embodiments, the cutoff
frequencies of such a filter are selected based on the measured heart
rate or respiratory rate of the subject under test. In some embodiments,
the cutoff frequencies of a filter are chosen based on the frequency
response of the hardware platform underlying patient monitoring system 10
(FIGS. 1 and 2). In some embodiments, a windowing operation is performed
at step 604 to suppress or amplify one or more portions of a signal
obtained at step 602.

[0110] Different processing operations may be applied to any one or both
of the first and second signals obtained at step 602 and/or any
components of a multi-component signal. For example, different operations
may be applied to a signal taken from a first body site and a signal
taken from a second body site.

[0111] Any of the operations described herein may be applied to a portion
or portions of an obtained signal. An operation may be broken into one or
more stages performed by one or more devices within signal processing
system 300 of FIG. 3 (which may itself be a part of patient monitoring
system 10 of FIGS. 1 and 2). For example, a filtering technique may be
applied by input signal generator 310 (FIG. 3) prior to passing the
resulting input signal 316 (FIG. 3) to processor 312 (FIG. 3), where the
input signal may undergo a transformation and/or the calculation of a
time derivative. Embodiments of the steps of flow diagram 600 may include
any of the operations described herein performed in any suitable order.

[0112] At step 606, a first parameter and a second parameter are
calculated for each respiratory modulation signal generated in step 604.
In some embodiments, the first and second parameters correspond to an
amplitude and a mean baseline of a respiratory modulation signal. In some
embodiments, a first parameter is calculated at step 606 based on
features of the respiratory modulation signal. A feature of a signal may
be any characterization of that signal, including for example, the
temporal location of an extremum (e.g., maxima or minima), the spatial
location of an extremum, or the amplitude of an extremum. In some
embodiments, a feature of a processed signal is a calculated quantity
based at least in part on a portion of the processed signal. For example,
a feature of a processed signal may be an average or weighted average of
the processed signal over a window, a baseline value over a window, a
magnitude or phase of a frequency component of a Fourier transform, a
magnitude or phase or scale of a continuous wavelet transform, or any
suitable calculated feature.

[0113] In some embodiments, only a portion or portions of a respiratory
modulation signal are analyzed to identify features of interest. For
example, certain segments of a signal may be identified, and only those
segments may be analyzed for the presence of certain features (e.g.,
extrema). Identifying segments of a signal may occur before or after any
one or more of the processing operations and thus the segments may be
identified prior to completing the processing operations. Focusing the
calculation of the first parameter on identified segments of the
respiratory modulation signals may improve the efficiency of carrying out
the steps of flow diagram 600 by reducing the time spent analyzing
portions of the signals that are less relevant to the information of
interest (e.g., the noisier regions).

[0114] In some embodiments, calculating a first parameter includes
identifying an amplitude of a respiratory modulation signal. For example,
as shown in FIG. 4(c), the maxima 410 and minima 412 of respiratory
modulation signal 404 can be identified and lines 414 and 416 may be
fitted to the successive maxima and minima, forming an envelope around
the signal. The amplitude may be defined as the height of the envelope
(i.e., the distance between lines 414 and 416). Alternatively, as shown
in FIG. 4(d), a baseline signal 418 of respiratory modulation signal 404
can be defined. The baseline signal 418 may be computed by low-pass
filtering the respiratory modulation signal at a frequency below that of
the respiration rate (e.g., 0.1 Hz) or by any other suitable method. The
distance from respiratory modulation signal 404 and the baseline signal
418 may be computed and averaged over a number of cycles of the
respiratory modulation signal. Other suitable methods for computing an
amplitude of respiratory modulation signal may also be employed.

[0115] Continuing with step 606, a second parameter is also calculated for
each of the respiratory modulation signals. This order of processing and
calculations is merely illustrative; it will be understood that either of
the processing steps and the first and second parameter calculating steps
may be performed in any suitable order or simultaneously.

[0116] In some embodiments, the second parameter calculated at step 606
includes one or more summary statistics of a respiratory modulation
signal. The second parameter may be calculated for each of the
respiratory modulation signals. In one embodiment, the second parameter
is the mean baseline value of the respiratory modulation signal. The
baseline may be computed as discussed above in connection with the
calculation of the first parameter and then averaged over a suitable time
window to form a mean baseline value. In another embodiment, the second
parameter may be a value associated with the mean amplitude of a
respiratory modulation signal. In some embodiments, the number of
amplitudes used to calculate the mean amplitude is predetermined. In some
embodiments, the number of amplitudes used to calculate the mean
amplitude is variable. In some embodiments, the mean amplitude is
calculated over a time window.

[0117] At step 608, a ratio is computed for each respiratory modulation
signal. The ratio may be the quotient of the first and second parameters
computed from that signal. In some embodiments, the ratio is the quotient
of the amplitude of the respiratory modulation signal and the mean
baseline value of the respiratory modulation signal. In some embodiments
a first respiratory modulation signal is based on a PPG signal
corresponding to a red wavelength and a second respiratory modulation
signal is based on a PPG signal corresponding to an infrared wavelength.
In some embodiments, logarithms of the first and second ratios are
calculated and stored in ROM 52 or RAM 54 (FIG. 1). Any other suitable
mathematical function may also be used.

[0118] At step 610, a ratio of ratios is calculated, which may be used to
determine a venous oxygen saturation value, as discussed in relation to
step 612. In some embodiments, the ratio of ratios is calculated by
dividing the logarithm of the first ratio obtained for the red PPG signal
by the logarithm of the second ratio obtained for the infrared PPG
signal. That is, a ratio of ratios, RoR, is calculated using the equation

RoR = ln ( R A / R B ) ln ( IR A /
IR B ) , ( 9 ) ##EQU00011##

where ln represents the logarithm operator, RA is the amplitude of
the baseline modulation component of a red PPG signal, RB is the
mean baseline value of the baseline modulation component of a red PPG
signal, RA/RB represents the first ratio corresponding to a red
PPG signal, IRA is the amplitude of the baseline modulation
component of an infrared PPG signal, IRB is the mean baseline value
of the baseline modulation component of an infrared PPG signal, and
IRA/IRB represents the second ratio corresponding to an
infrared PPG signal. The calculation of the ratio of ratios may be
performed by processor 312 (FIG. 3) and the resulting numerical value may
be stored in ROM 52 or RAM 54 (FIG. 1). In sonic embodiments, the ratio
of ratios is calculated without taking a logarithm of the first ratio
corresponding to a red PPG signal or the second ratio corresponding to an
infrared PPG signal. In yet other embodiments, RoR can be computed
alternatively as a ratio of AC/DC signals or a ratio of the derivatives,
as described in equation (7); or the values of RA, RB,
IRA, IRB in equation (9) can be taken from points in time
corresponding to local maxima and minima or other signal points along
baseline modulation.

[0119] At step 612, information about the subject based at least in part
on the ratio of ratios is determined. In some embodiments, information
determined at step 612 is physiological information. For example,
physiological information determined at step 612 may include venous
oxygen saturation.

[0120] In some embodiments, the ratio of ratios calculated at step 610 is
used to determine the subject's venous oxygen saturation by using a
look-up table. The look-up table may include entries associating a
numerical value of the ratio of ratios to a value of venous oxygen
saturation. For example, a ratio of ratios value of about 1.1 may
correspond to a venous oxygen saturation value of about 80%; or when
sensor 18 (FIG. 1) is placed at the subject's finger, the ratio of ratios
value may fall in the range 0.5-0.7 which may correspond to a venous
oxygen saturation value in the range 90-99%; or when sensor 18 (FIG. 1)
is placed at the subject's chest wall, the ratio of ratios value may fall
in the range 0.4-1.3 which may correspond to a venous oxygen saturation
value in the range 70-100%. In some embodiments, the entries of the
look-up table are predetermined or are determined based on calibrating
test ratio of ratios values to sample venous oxygen saturation values. In
some embodiments, the entries of the look-up table account for the
ambient temperature by calibrating the venous oxygen saturation values
appropriately. For example, a given ratio of ratios value that
corresponds to a given venous oxygen saturation value at a given
temperature may correspond to a venous oxygen saturation value higher or
lower than the given venous oxygen saturation value depending on whether
the temperature is higher or lower than the given temperature. The
look-up table may be stored in ROM 52 or RAM 54 (FIG. 1) or may be stored
in external storage (not shown). In some embodiments, the look-up table
is a Server Query Language (SQL) or any other appropriate database.
Alternatively, the subject's venous oxygen saturation can be computed
from the ratio of ratio values according to a numerical equation
following the format of the equation shown immediately below equation (5)
or other suitable function that can be used to describe the curve
corresponding to the relationship between the ratio of ratios and venous
oxygen saturation.

[0121] In some embodiments, the subject's arterial oxygen saturation is
determined in a manner similar to the process described in flow chart
600. For example, at step 606 a pulsatile component of each of the
plurality of signals may be identified based on the processing techniques
described above. Steps 608-612 may then be performed on the pulsatile
components, identified respectively for the red PPG signal and the
infrared PPG signal, for determining the subject's arterial oxygen
saturation.

[0122] In some embodiments, the subject's arterial oxygen saturation and
venous oxygen saturation are determined in parallel by processing
equipment. Parallel determination of the subject's arterial oxygen
saturation and venous oxygen saturation allows the monitoring of a
differential desaturation characteristic between the subject's arterial
oxygen saturation and venous oxygen saturation. Monitoring and comparing
the differential desaturation advantageously allows for a more robust
indication of a subject's oxygen saturation. In some embodiments, the
subject's arterial oxygen saturation is determined using red and infrared
PPG signals obtained from a sensor placed at a first site on the subject
and the subject's venous oxygen saturation is determined using red and
infrared PPG signals obtained from a sensor placed at a second site on
the subject.

[0123] After information about the subject is determined at step 612, the
information determined may be output to an output device through a
graphical representation, quantitative representation, qualitative
representation, or combination of representations via output 314 (FIG. 3)
and may be controlled by processor 312 (FIG. 3). In some embodiments,
output 314 (FIG. 3) transmits physiological information by any means and
through any format useful for informing a patient, a care provider, or a
third party, of a patient's status and records the physiological
information to a storage medium. Quantitative and/or qualitative
information provided by output 314 (FIG. 3) may be displayed on a display
(e.g., display 28 of FIG. 1). A graphical representation may be displayed
in one, two, or more dimensions and may be fixed or change with time. A
graphical representation may be further enhanced by changes in color,
pattern, or any other visual representation. Output 314 (FIG. 3) may
communicate the information by performing at least one of the following:
presenting a screen on a display; presenting a message on a display;
producing a tone or sound; changing a color of a display or a light
source; producing a vibration; and sending an electronic message. Output
314 (FIG. 3) may perform any of these actions in a device close to a
patient, or at a mobile or remote monitoring device as described
previously. In some embodiments, output 314 (FIG. 3) produces a
continuous tone or beeping whose frequency changes in response to changes
in a process of interest, such as a physiological process. In some
embodiments, output 314 (FIG. 3) produces a colored or flashing light
that changes in response to changes in a physiological process of
interest.

[0124] After or during the information determination of step 612, the
steps of flow diagram 600 may be repeated. New signals may be obtained,
or the information determination may continue on another portion of one
or more of the previously obtained signal(s). In some embodiments,
processor 312 (FIG. 3) continuously or periodically performs steps
602-612 and updates the information (e.g., as the patient's condition
changes). The process may repeat indefinitely, until there is a command
to stop the monitoring and/or until some detected event occurs that is
designated to halt the monitoring process. For example, it may be
desirable to halt a monitoring process when a detected noise has become
too great, a measurement quality has become too low, or, in a patient
monitoring setting, when a patient has undergone a change in condition
that can no longer be sufficiently well-monitored in a current monitoring
configuration. In some embodiments, processor 312 (FIG. 3) performs the
steps of flow diagram 600 at a prompt from a care provider via user
inputs 56 (FIG. 2). In some embodiments, processor 312 (FIG. 3) performs
the steps of flow diagram 600 at intervals that change according to
patient status. For example, the steps of flow diagram 600 may be
performed more often when a patient is undergoing rapid changes in
physiological condition, and performed less often as the patient's
condition stabilizes.

[0125] The steps of flow diagram 600 may be executed over a sliding window
of a signal. For example, the steps of flow diagram 600 may involve
analyzing the previous samples of the signal, or the samples of the
signal obtained in the previous units of time. The length of the sliding
window over which the steps of flow diagram 600 is executed may be fixed
or dynamic. In some embodiments, the length of the sliding window is
based at least in part on the noise content of a signal. For example, the
length of the sliding window may increase with decreasing measurement
quality and/or increasing noise, as may be determined by a measurement
quality assessment and/or a noise assessment. A subject's venous oxygen
saturation may be monitored continuously using a moving PPG signal. PPG
signal detection means may include a pulse oximeter and associated
hardware, software, or both. A processor may continuously analyze the
signal from the PPG signal detection means in order to continuously
monitor a subject's venous oxygen saturation.

[0126] Any number of computational and/or optimization techniques may be
performed in conjunction with the techniques described herein. For
example, any known information regarding the physiological status of the
patient may be stored in memory (e.g., ROM 52 or RAM 54 of FIG. 2). Such
known information may be keyed to the characteristics of the patient,
which may be input via user inputs 56 (FIG. 2) and used by monitor 14
(FIGS. 1 and 2) to, for example, query a look-up table and retrieve the
appropriate information. Additionally, any of the calculations and
computations described herein may be optimized for a particular hardware
implementation, which may involve implementing any one or more of a
pipelining protocol, a distributed algorithm, a memory management
algorithm, or any suitable optimization technique.

[0127] The steps of flow chart 600 describe using the ratio of ratios to
estimate a subject's venous oxygen saturation. The ratio of ratios may
also be used to determine and evaluate the signal quality of the PPG
signal itself, as discussed in relation to FIGS. 7(a)-11. For example,
the ratio of ratios may be used to determine the likelihood that
modulations in PPG signals are caused by respiration, as opposed to being
an artifact of a patient's motion.

[0128] FIG. 7(a) shows an illustrative plot 700 of normalized respiration
modulation signals derived from red and infrared PPG signals in
accordance with some embodiments. The PPG signals may be obtained from,
for example, sensor 12 of FIGS. 1 and 2, or sensor 318 of FIG. 3. One or
both of the PPG signals may be provided as part of input signal 316 (FIG.
3) from sensor 318. The PPG signals from which illustrative plot 700 is
derived are obtained from a test time series of a subject's breathing.
During the test, the subject breathed at 15 breaths per minute (4-second
breaths). Different types of breathing resulted in sections of varying
amplitudes in plot 700. At the beginning of the test, the subject
breathed freely, without resistance, as indicated by section 702 of plot
700. In the next part of the test, the subject breathed through a
resistive element for 60 seconds, as indicated by section 704 of plot
700. Examples of resistive elements include a small bore tube, a hand
partially placed over the mouth, or a porous material placed over the
mouth. Such resistive elements are typically placed in or over the mouth
and the nose is closed off to force the subject to breathe only through
the element. After the resistive breathing, the subject once again
breathed freely, as indicated by section 712 of plot 700. Later in the
test, the subject breathed while moving a hand from a high to low
position in 4-second cycles. The effect of this motion on the PPG signals
is seen in section 714 of plot 700.

[0129] The PPG signals obtained during the test time series may be
low-pass filtered, illustratively at 0.5 Hz, in order to remove the
cardiac pulse components but retain the respiration components. Baseline
signals may be generated by low-pass filtering the PPG signals at another
frequency, illustratively at 0.1 Hz. For each PPG signal, the baseline
signal may be removed from the respiration component, and then the result
may be divided by the baseline signal to give a normalized respiration
modulation signal for each PPG signal, as shown in plot 700 of FIG. 7(a).
The red PPG normalized respiration modulation signal 708 and the infrared
PPG normalized respiration modulation signal 710 in FIG. 7(a) are more
easily distinguished from one another in zoomed-in portion 706 of plot
700. In some embodiments, the normalized respiration modulation signals
are processed according to the illustrative steps of flow chart 600 (FIG.
6).

[0130] FIG. 7(b) shows an illustrative ratio signal 720 obtained by
dividing the red and infrared signals of FIG. 7(a) by each other in
accordance with some embodiments. For example, the red PPG normalized
respiration modulation signal 708 in FIG. 7(a) may be divided by the
infrared PPG normalized respiration modulation signal 710 in FIG. 7(a).
The division may be performed by processing equipment such as processor
316 of FIG. 3, microprocessor 48 of FIG. 2, or any suitable processing
device. If the red PPG normalized respiration modulation signal 708 is
divided by the infrared PPG normalized respiration modulation signal 710,
discontinuities in ratio signal 720 may appear where infrared PPG
normalized respiration modulation signal 710 goes through zero. If the
infrared PPG normalized respiration modulation signal 710 is divided by
the red PPG normalized respiration modulation signal 708, discontinuities
in ratio signal 720 may appear where red PPG normalized respiration
modulation signal 708 goes through zero.

[0131] FIG. 7(c) shows an illustrative filtered ratio signal 740 obtained
by taking a median value of the illustrative ratio signal 720 of FIG.
7(b) over a 20-second window in accordance with some embodiments.
Filtered ratio signal 740 exhibits distinctly different levels, indicated
by sections 742 and 744, during the motion and no-motion portions of the
test time series used to generate FIGS. 7(a)-(b). In some embodiments,
filtered ratio signal 740 is used as an indication of modulations in one
or more PPG signals being wholly or partly due to motion. For example,
low values as in section 742 of filtered ratio signal 740 may correspond
to modulations due to respiration. High values as in section 744 of
filtered ratio signal 740 may correspond to modulations due wholly or
partly to the subject's motion.

[0132] Various methods may be used to filter ratio signal 720 of FIG.
7(b). In some embodiments, the mean of a percentile range is taken as the
ratio metric. FIG. 8 shows an illustrative mean of the 40th to
60th percentile range of values of the illustrative ratio signal of
FIG. 7(b) in accordance with some embodiments. For example, the 40th
to 60th percentile range of values of ratio signal 720 may be taken
over a 20-second window. Filter settings, such as percentile ranges for
obtaining a clipped mean value, and window settings, such as the length
of a window, may vary with different embodiments.

Arterial and Venous Ratios

[0133] A ratio of ratios, calculated for example by performing the steps
of FIG. 6 on the physiological signals of FIG. 7(a), may be used to
evaluate physiological signals. FIG. 9 is a flow chart 900 of
illustrative steps for using a ratio of ratios to analyze a physiological
signal obtained from a subject, such as determining whether modulations
in the signal are due to respiration or motion, in accordance with some
embodiments. FIG. 9 illustrates how to obtain a signal (step 902), how to
calculate first and second ratio values based on the signal (steps 904
and 906), and how to process the first and second ratio values to make a
determination about the signal or subject (steps 908 and 910). The
illustrative steps of flow chart 900 may be performed on the normalized
respiration modulation signals of FIG. 7(a), or on any signals acquired
at any external or internal body site. The steps of flow chart 900 may be
performed by processing equipment such as processor 316 of FIG. 3,
microprocessor 48 of FIG. 2, or any suitable processing device. The steps
of flow chart 900 may be performed by a digital processing device, or
implemented in analog hardware. It will be noted that the steps of flow
chart 900 may be performed in any suitable order, and one or more steps
may be omitted entirely according to the context and application.

[0134] At step 902, a physiological signal is obtained from a subject. The
signal may be a PPG signal and may be obtained from any suitable source
(e.g., sensor 12 of FIG. 2) using any suitable technique. A sensor from
which a signal is obtained may include any of the physiological sensors
described herein, or any other sensor. An obtained signal may be signal
402 as shown in FIG. 4(a). An obtained signal may include multiple
signals, for example, in the form of a multi-dimensional vector signal or
a frequency-multiplexed or time-multiplexed signal. In some embodiments,
the physiological signal obtained at step 902 includes two or more PPG
signals, which may be measured at two or more respective body sites of a
subject.

[0135] The physiological signal obtained at step 902 may include first and
second physiological signals obtained as input signals. In some
embodiments, a first signal is a red PPG signal corresponding to a red
wavelength, and a second signal is a PPG signal corresponding to an
infrared wavelength. The red and infrared wavelengths may correspond to
those used in traditional pulse oximetry, or entirely different
wavelengths may be used. In some embodiments, each of the first and
second signals includes a cardiac pulsatile component and a baseline
modulation component, such as pulsatile component 406 of FIG. 4(a) and
baseline modulation component 404 of FIG. 4(b). In some embodiments,
first and second signals are obtained by first and second sensors located
at approximately the same body site of a subject. In some embodiments,
first and second signals are obtained by first and second sensors located
at different body sites of a subject. For example, first and second
signals included in a plurality of signals may be electronic signals from
pulse oximetry sensors located at two different body sites of a subject.
It will be noted that the steps of flow diagram 900 may be applied to any
number of obtained signals in accordance with the techniques described
herein.

[0136] At step 904, a first ratio value indicative of a respiration
modulation in the physiological signal obtained at step 902 is obtained.
The first ratio value may be obtained in conjunction with the obtaining
at step 902, or after the physiological signal is obtained at step 902.
The first ratio value may be obtained by performing one or more of steps
604-606 as discussed above in relation to FIG. 6. For example, the first
parameter mentioned in step 606 may be an amplitude of a
respiration-induced baseline modulation in the obtained physiological
signal, and the second parameter in step 606 may be a mean amplitude of
the respiration-induced baseline modulation. The physiological signal
obtained at step 902 may be filtered around a respiration rate in order
to derive the respiration-induced baseline modulation, which may
represent the modulation of light transmission corresponding to venous
blood. The filtering may better distinguish baseline modulations,
facilitating the calculation of ratio values. In some embodiments, one or
more time derivatives of the obtained physiological signal are used to
calculate the first ratio value. Calculation of the first ratio value is
further discussed in relation to FIG. 10.

[0137] In some embodiments, the first ratio value obtained in step 904 is
stored in ROM 52 or RAM 54 (FIG. 1). In some embodiments, the first ratio
value obtained in step 904 is processed further or utilized immediately
by processor 312 (FIG. 3) for determining information about the subject's
physiological condition.

[0138] At step 906, a second ratio value indicative of a pulsatile
component in the physiological signal obtained at step 902 is obtained.
The second ratio value may be obtained in conjunction with the obtaining
at step 902, or after the physiological signal is obtained at step 902.
The second ratio value may be obtained simultaneously with the first
ratio value, or after the first ratio value has been obtained. In some
embodiments, the second ratio value is computed from cardiac pulse
components of the physiological signal obtained in step 902 in normal
oximetry fashion. In some embodiments, one or more time derivatives of
the obtained physiological signal are used to calculate the second ratio
value. Calculation of the second ratio value is further discussed in
relation to FIG. 10.

[0139] In some embodiments, the second ratio value obtained in step 906 is
stored in ROM 52 or RAM 54 (FIG. 1). In some embodiments, the second
ratio value obtained in step 906 is processed further or utilized
immediately by processor 312 (FIG. 3) for determining information about
the subject's physiological condition.

[0140] At step 908, the first ratio value obtained in step 904 is compared
to the second ratio value obtained in step 906. The comparison of the
first and second ratio values may include deriving a signal quality
metric from the first ratio value and the second ratio value. In some
embodiments, the signal quality metric is a function, such as a combined
ratio, of the first ratio value and the second ratio value. For example,
the signal quality metric may be calculated by dividing the first ratio
value by the second ratio value. In some embodiments, the signal quality
metric is a function of the value of an arterial ratio value (e.g.,
arterial ratio of ratios) and a venous ratio value (e.g., venous ratio of
ratios).

[0141] At step 910, a determination is made based on the comparison of the
first ratio value to the second ratio value performed in step 908. In
some embodiments, the determination is a likelihood that baseline
modulations in the physiological signal obtained in step 902 are caused
by respiration as opposed to being caused by the subject's movement. A
first ratio value that is similar to the second ratio value may be a
positive indication of the modulations being due to respiration, assuming
that minimal oxygen demand takes place at the site (e.g., finger) where
the physiological signal is obtained and that the arterial and venous
blood therefore have very similar values. In some embodiments, the first
ratio value is a venous RoR and the second ratio value is an arterial
RoR. It is known that an RoR of unity may be an indication of a movement
artifact. Hence, the further the venous RoR and the arterial RoR are from
unity and the more similar the venous RoR and the arterial RoR are to
each other, the higher the confidence in the computed arterial and venous
oxygen saturations.

[0142] In some embodiments, the determination made at step 910 is based on
a difference between a signal quality metric, such as the signal quality
metric derived in step 908, and a threshold value. The threshold value
may be retrieved from a look-up table stored in memory, such as ROM 52 or
RAM 54 (FIG. 1), or external storage.

[0143] In some embodiments, the threshold value is a finger oxygen usage
measure derived from a long-term difference between respiration and
pulsatile modulations in data collected from the subject over time. The
finger oxygen usage measure may be expected to be relatively constant
over time even as arterial SpO2 changes. A physiological signal, such as
a PPG signal, obtained at a subject's finger is useful for determining
whether a modulation in the signal is due to respiration or movement
because the oxygen content of the arterial and venous blood at the finger
may be very similar due to oxygen demand at the fingertip being
relatively small. Any sudden deviations of a signal quality metric, such
as the signal quality metric derived in step 908, from the established
finger oxygen usage measure may indicate that modulations in the
physiological signal obtained in step 902 are due to the subject's
motion. In other words, a short term finger oxygen usage measure that is
similar to the long term average may indicate that recent venous/baseline
modulations are likely due to the subject's respiration.

[0144] In some embodiments, a pulse oximetry system includes an indication
of the first ratio value calculated in step 904 relative to the second
ratio value calculated in step 906. The indication may be of a difference
between a threshold value and a combined ratio of ratios. The combined
ratio of ratios may include a function of a first ratio of ratios (e.g.,
venous RoR) and a second ratio of ratios (e.g., arterial RoR). An
indicator, which may appear on display 28 of FIG. 1 or display 20 of FIG.
2, or any other display that is communicatively coupled to the pulse
oximetry system, may indicate whether baseline modulation in at least one
of the first and second wavelength components (e.g., red and IR
wavelength components) is due to respiration or motion of the subject.
The indicator may indicate that baseline modulation in at least one of
the first and second wavelength components is due to respiration of the
subject when there are small deviations of the combined ratio of ratios
from a threshold value. The indicator may indicate that baseline
modulation in at least one of the first and second wavelength components
is due to motion of the subject when there are large deviations of the
combined ratio of ratios from the threshold value. In some embodiments,
the indicator includes a visible or audible alarm that is triggered when
a baseline modulation in at least one of the first and second wavelength
components is due to motion of the subject.

[0145] Calculation of the first and second ratio values of FIG. 9 is
further discussed with respect to FIG. 10. FIG. 10 is a flow chart 1000
of illustrative steps for using ratios to analyze a physiological signal
obtained from a subject in accordance with some embodiments. FIG. 10
illustrates how to calculate a first ratio of ratios (steps 1002, 1004,
and 1006) and a second ratio of ratios (steps 1008, 1010, and 1012), and
then how to calculate a combined ratio of ratios (step 1014). The
illustrative steps of flow chart 1000 may be performed as part of or in
addition to some of the illustrative steps of flow chart 900, and may be
performed on any signals acquired at any external or internal body site.
The steps of flow chart 1000 may be performed by processing equipment
such as processor 316 of FIG. 3, microprocessor 48 of FIG. 2, or any
suitable processing device. The steps of flow chart 1000 may be performed
by a digital processing device, or implemented in analog hardware. It
will be noted that the steps of flow chart 1000 may be performed in any
suitable order, and one or more steps may be omitted entirely according
to the context and application.

[0146] At step 1002, a first numerator value is calculated from a first
respiratory modulation signal derived from a physiological signal of a
first wavelength. The physiological signal of a first wavelength may be,
for example, a PPG signal corresponding to a red wavelength. The first
numerator may be computed by dividing an amplitude of the first
respiratory modulation by a mean baseline value of the first respiratory
modulation. In some embodiments, the amplitude is a mean peak to trough
value of the first respiratory modulation over a window of time. The time
window may vary based on the respiration rate of the patient. The mean
baseline may be a low-pass filtered version of the respiratory modulation
signal. Methods of calculating amplitudes and mean baseline values are
discussed in more detail above in connection with FIG. 6. In some
embodiments, the first numerator value calculation excludes points where
the baseline of the first respiratory modulation signal is zero.

[0147] At step 1004, a first denominator value is calculated from a second
respiratory modulation signal derived from a physiological signal of a
second wavelength. The physiological signal of a second wavelength may
be, for example, a PPG signal corresponding to an infrared wavelength.
The second numerator is computed by dividing an amplitude of the second
respiratory modulation by a baseline value of the second respiratory
modulation. In some embodiments, the amplitude of the second respiratory
signal is a mean peak to trough value signal over a window of time. The
time window may vary based on the respiration rate of the patient. In
some embodiments, the second denominator value excludes points where the
baseline value is zero.

[0148] At step 1006, a first ratio of ratios is calculated using the first
numerator value obtained at step 1002 and the first denominator value
obtained at step 1004. For example, the first numerator value may be
divided by the first denominator value to obtain the first ratio of
ratios. In some embodiments, calculating the first ratio of ratios
involves calculating a logarithmic term. For example, the ratio of ratios
may be the quotient of the logarithm of the first numerator and the
logarithm of the first denominator. The first ratio of ratios may be
indicative of the oxygen saturation of the subject's venous blood.

[0149] At step 1008, a second numerator value is calculated by dividing a
first amplitude of a first pulsatile component in a first wavelength
component of the obtained physiological signal by a first mean amplitude
of the first pulsatile component. The first wavelength component may be,
for example, a component of a PPG signal corresponding to a red
wavelength.

[0150] At step 1010, a second denominator value is calculated by dividing
a second amplitude of a second pulsatile component in a second wavelength
component of the obtained physiological signal by a second mean amplitude
of the second pulsatile component. The second wavelength component may
be, for example, a component of a PPG signal corresponding to an IR
wavelength.

[0151] At step 1012, a second ratio of ratios is calculated using the
second numerator value obtained in step 1008 and the second denominator
value obtained in step 1010. For example, the second numerator value may
be divided by the second denominator value to obtain the second ratio of
ratios. In some embodiments, calculating the second ratio of ratios
involves calculating a logarithmic term. For example, the natural
logarithm of the quotient of the second numerator value and the second
denominator value may be calculated to obtain the second ratio of ratios.
The second ratio of ratios may be indicative of the oxygen saturation of
the subject's arterial blood.

[0152] At step 1014, a combined ratio of ratios, which includes comparing
the first ratio of ratios calculated in step 1006 and the second ratio of
ratios calculated in step 1012, is calculated. For example, the combined
ratio of ratios may be calculated by dividing the first ratio of ratios
by the second ratio of ratios. In some embodiments, the combined ratio of
ratios is a function of the value of an arterial ratio of ratios and a
venous ratio of ratios. For example, the combined ratio of ratios may be
calculated by dividing the natural logarithm of the arterial ratio of
ratios by the natural logarithm of the venous ratio of ratios, as in
equation (9), discussed in relation to FIG. 6.

[0153]FIG. 11 is a flow chart 1100 illustrating steps for a ratio method
of analyzing a physiological signal obtained from a subject, where the
respiration modulation components of the obtained physiological signal
are normalized in accordance with some embodiments. In particular, FIG.
11 illustrates how to obtain a signal (step 1102), how to filter first
and second wavelength components (steps 1104 and 1106), and then how to
normalize respiration modulation components and use them to calculate a
ratio (steps 1108 and 1110). The illustrative steps of flow chart 1100
may be performed to obtain the normalized respiration modulation signals
of FIG. 7(a) and may be performed as part of in addition to, or instead
of some of the illustrative steps of flow charts 900 or 1000. The
illustrative steps of flow chart 1100 may be performed on any signals
acquired at any external or internal body site. The steps of flow chart
1100 may be performed by processing equipment such as processor 316 of
FIG. 3, microprocessor 48 of FIG. 2, or any suitable processing device.
The steps of flow chart 1100 may be performed by a digital processing
device, or implemented in analog hardware. It will be noted that the
steps of flow chart 1100 may be performed in any suitable order, and one
or more steps may be omitted entirely according to the context and
application.

[0154] At step 1102, a physiological signal is obtained from a subject.
The signal may be a PPG signal and may be obtained from any suitable
source (e.g., sensor 12 of FIG. 2) using any suitable technique. A sensor
from which a signal is obtained may include any of the physiological
sensors described herein, or any other sensor. An obtained signal may be
signal 402 as shown in FIG. 4(a). An obtained signal may include multiple
signals, for example, in the form of a multi-dimensional vector signal or
a frequency-multiplexed or time-multiplexed signal. In some embodiments,
the physiological signal obtained at step 1102 includes two or more PPG
signals, which may be measured at two or more respective body sites of a
subject.

[0155] The physiological signal obtained at step 1102 may include first
and second physiological signals obtained as input signals. In some
embodiments, a first signal is a red PPG signal corresponding to a red
wavelength, and a second signal is a PPG signal corresponding to an
infrared wavelength. The red and infrared wavelengths may correspond to
those used in traditional pulse oximetry, or entirely different
wavelengths may be used. In some embodiments, each of the first and
second signals includes a cardiac pulsatile component and a baseline
modulation component, such as pulsatile component 406 of FIG. 4(a) and
baseline modulation component 404 of FIG. 4(b). In some embodiments,
first and second signals are obtained by first and second sensors located
at approximately the same body site of a subject. In some embodiments,
first and second signals are obtained by first and second sensors located
at different body sites of a subject. For example, first and second
signals included in a plurality of signals may be electronic signals from
pulse oximetry sensors located at two different body sites of a subject.
It will be noted that the steps of flow diagram 1100 may be applied to
any number of obtained signals in accordance with the techniques
described herein.

[0156] At step 1104, first and second wavelength components of the
physiological signal obtained at step 1102 are filtered to remove cardiac
pulse modulation components while retaining respiration modulation
components--these are the first and second respiratory modulation
signals. In some embodiments, the physiological signal obtained at step
902 is filtered based on a respiration rate in order to derive the
respiration-induced baseline modulation components. For example, a
physiological signal may be low-pass filtered at 0.5 Hz to remove cardiac
modulations while retaining respiratory modulations. The retained
modulation represents the modulation of light transmission corresponding
to venous blood.

[0157] At step 1106, the first and second respiratory modulation signals
are filtered to generate first and second baseline signals. In some
embodiments, baseline signals are generated by low-pass filtering the
first and second respiratory modulation signals at a frequency below the
respiration rate. For example, the first and second respiratory
modulation signals may be low-pass filtered at 0.1 Hz.

[0158] At step 1108, respiratory modulation signals obtained in step 1106
are normalized to generate first and second normalized respiratory
modulation signals. Normalized signals are illustrated in plot 700 of
FIG. 7(a). In some embodiments, a normalized signal is computed by taking
the difference between a respiratory modulation signal and its baseline
signal and then dividing this difference by the baseline signal.

[0159] At step 1110, a ratio of normalized respiration modulation
components is calculated. The ratio may be calculated by dividing the
first normalized respiratory modulation signal by the second normalized
respiratory modulation signal or vice versa. In another embodiment, the
ratio is calculated by dividing the logarithm of the first normalized
respiratory modulation signal by the second normalized respiratory
modulation signal. The ratio may be calculated by processing equipment
such as processor 316 of FIG. 3, microprocessor 48 of FIG. 2, or any
suitable processing device. The calculated ratio may be indicative of
whether motion of the subject has caused at least one of the first and
second respiration modulation components. Ratios of normalized
respiration modulation components are illustrated in and discussed above
in relation to FIGS. 7(b)-(c) and FIG. 8.

[0160] In some embodiments, respiration modulation components from a PPG
signal, such as the physiological signal obtained from a subject in any
of steps 602, 902, and 1102, may be further identified and evaluated by
transforming the respiration modulation components using a continuous
wavelet transform. Information derived from the transform of the
respiration modulation components (i.e., in wavelet space) may be used to
provide measurements of one or more physiological parameters, or to
determine whether modulations in the signal are due to respiration or
motion.

[0161] The continuous wavelet transform of a signal x(t) in accordance
with the present disclosure may be defined as

where w*(t) is the complex conjugate of the wavelet function ψ(t), a
is the dilation parameter of the wavelet and b is the location parameter
of the wavelet. The transform given by equation (10) may be used to
construct a representation of a signal on a transform surface. The
transform may be regarded as a time-scale representation. Wavelets are
composed of a range of frequencies, one of which may be denoted as the
characteristic frequency of the wavelet, where the characteristic
frequency associated with the wavelet is inversely proportional to the
scale a. One example of a characteristic frequency is the dominant
frequency. Each scale of a particular wavelet may have a different
characteristic frequency. The underlying mathematical detail required for
the implementation within a time-scale can be found, for example, in Paul
S. Addison, The Illustrated Wavelet Transform Handbook (Taylor & Francis
Group 2002), which is hereby incorporated by reference herein in its
entirety.

[0162] The continuous wavelet transform decomposes a signal using
wavelets, which are generally highly localized in time. The continuous
wavelet transform may provide a higher resolution relative to discrete
transforms, thus providing the ability to garner more information from
signals than typical frequency transforms such as Fourier transforms (or
any other spectral techniques) or discrete wavelet transforms. Continuous
wavelet transforms allow for the use of a range of wavelets with scales
spanning the scales of interest of a signal such that small scale signal
components correlate well with the smaller scale wavelets and thus
manifest at high energies at smaller scales in the transform. Likewise,
large scale signal components correlate well with the larger scale
wavelets and thus manifest at high energies at larger scales in the
transform. Thus, components at different scales may be separated and
extracted in the wavelet transform domain. Moreover, the use of a
continuous range of wavelets in scale and time position allows for a
higher resolution transform than is possible relative to discrete
techniques.

[0163] In addition, transforms and operations that convert a signal or any
other type of data into a spectral (i.e., frequency) domain necessarily
create a series of frequency transform values in a two-dimensional
coordinate system where the two dimensions may be frequency and, for
example, amplitude. For example, any type of Fourier transform would
generate such a two-dimensional spectrum. In contrast, wavelet
transforms, such as continuous wavelet transforms, are required to be
defined in a three-dimensional coordinate system and generate a surface
with dimensions of time, scale and, for example, amplitude. Hence,
operations performed in a spectral domain cannot be performed in the
wavelet domain; instead the wavelet surface must be transformed into a
spectrum (i.e., by performing an inverse wavelet transform to convert the
wavelet surface into the time domain and then performing a spectral
transform from the time domain). Conversely, operations performed in the
wavelet domain cannot be performed in the spectral domain; instead a
spectrum must first be transformed into a wavelet surface (i.e., by
performing an inverse spectral transform to convert the spectral domain
into the time domain and then performing a wavelet transform from the
time domain). Nor does a cross-section of the three-dimensional wavelet
surface along, for example, a particular point in time equate to a
frequency spectrum upon which spectral-based techniques may be used. At
least because wavelet space includes a time dimension, spectral
techniques and wavelet techniques are not interchangeable. It will be
understood that converting a system that relies on spectral domain
processing to one that relies on wavelet space processing would require
significant and fundamental modifications to the system in order to
accommodate the wavelet space processing (e.g., to derive a
representative energy value for a signal or part of a signal requires
integrating twice, across time and scale, in the wavelet domain while,
conversely, one integration across frequency is required to derive a
representative energy value from a spectral domain). As a further
example, to reconstruct a temporal signal requires integrating twice,
across time and scale, in the wavelet domain while, conversely, one
integration across frequency is required to derive a temporal signal from
a spectral domain. It is well known in the art that, in addition to or as
an alternative to amplitude, parameters such as energy density, modulus,
and phase, among others, may all be generated using such transforms and
that these parameters have distinctly different contexts and meanings
when defined in a two-dimensional frequency coordinate system rather than
a three-dimensional wavelet coordinate system. For example, the phase of
a Fourier system is calculated with respect to a single origin for all
frequencies while the phase for a wavelet system is unfolded into two
dimensions with respect to a wavelet's location (often in time) and
scale.

[0164] The energy density function of the wavelet transform, the
scalogram, is defined as

S(a,b)=|T(a,b)|2 (11)

where `∥` is the modulus operator. The scalogram may be resealed
for useful purposes. One common resealing is defined as

S R ( a , b ) = | T ( a , b ) | 2 a (
12 ) ##EQU00013##

and is useful for defining ridges in wavelet space when, for example, the
Morlet wavelet is used. Ridges are defined as the locus of points of
local maxima in the plane. Any reasonable definition of a ridge may be
employed in the method. Also included as a definition of a ridge herein
are paths displaced from the locus of the local maxima. A ridge
associated with only the locus of points of local maxima in the plane is
labeled a "maxima ridge."

[0165] For implementations requiring fast numerical computation, the
wavelet transform may be expressed as an approximation using Fourier
transforms. Pursuant to the convolution theorem, because the wavelet
transform is the cross-correlation of the signal with the wavelet
function, the wavelet transform may be approximated in terms of an
inverse FFT of the product of the Fourier transform of the signal and the
Fourier transform of the wavelet for each required a scale and then
multiplying the result by {square root over (a)}.

[0166] In the discussion of the technology which follows herein, the
"scalogram" may be taken to include all suitable forms of resealing
including, but not limited to, the original unsealed wavelet
representation, linear resealing, any power of the modulus of the wavelet
transform, or any other suitable resealing. In addition, for purposes of
clarity and conciseness, the term "scalogram" shall be taken to mean the
wavelet transform, T(a,b) itself, or any part thereof. For example, the
real part of the wavelet transform, the imaginary part of the wavelet
transform, the phase of the wavelet transform, any other suitable part of
the wavelet transform, or any combination thereof is intended to be
conveyed by the term "scalogram."

[0167] A scale, which may be interpreted as a representative temporal
period, may be converted to a characteristic frequency of the wavelet
function. The characteristic frequency associated with a wavelet of
arbitrary a scale is given by

f = f c a ( 13 ) ##EQU00014##

where fc, the characteristic frequency of the mother wavelet (i.e.,
at a=1), becomes a scaling constant and f is the representative or
characteristic frequency for the wavelet at arbitrary scale a.

[0168] Any suitable wavelet function may be used in connection with the
present disclosure. One of the most commonly used complex wavelets, the
Morlet wavelet, is defined as:

ψ(t)=π-1/4(e.sup.i2πf0t-e-(2πf0.su-
p.)2.sup./2)e-t2.sup./2 (14)

where f0 is the central frequency of the mother wavelet. The second
term in the parenthesis is known as the correction term, as it corrects
for the non-zero mean of the complex sinusoid within the Gaussian window.
In practice, it becomes negligible for values of f0>>0 and can
be ignored, in which case, the Morlet wavelet can be written in a simpler
form as

ψ ( t ) = 1 π 1 / 4 i 2 π
f 0 t - t 2 / 2 ( 15 )
##EQU00015##

[0169] This wavelet is a complex wave within a scaled Gaussian envelope.
While both definitions of the Morlet wavelet are included herein, the
function of equation (15) is not strictly a wavelet as it has a non-zero
mean (i.e., the zero frequency term of its corresponding energy spectrum
is non-zero). However, it will be recognized by those skilled in the art
that equation (15) may be used in practice with f0>>0 with
minimal error and is included (as well as other similar near wavelet
functions) in the definition of a wavelet herein. A more detailed
overview of the underlying wavelet theory, including the definition of a
wavelet function, can be found in the general literature. Discussed
herein is how wavelet transform features may be extracted from the
wavelet decomposition of signals. For example, wavelet decomposition of
PPG signals may be used to provide clinically useful information within a
medical device.

[0170] Pertinent repeating features in a signal give rise to a time-scale
band in wavelet space or a resealed wavelet space. For example, the pulse
component of a PPG signal produces a dominant band in wavelet space at or
around the pulse frequency. FIGS. 12(a) and (b) show two views of an
illustrative scalogram derived from a PPG signal in accordance with some
embodiments. The figures show an example of the band caused by the pulse
component in such a signal. The pulse band is located between the dashed
lines in the plot of FIG. 12(a). The band is formed from a series of
dominant coalescing features across the scalogram. This can be clearly
seen as a raised band across the transform surface in FIG. 12(b) located
within the region of scales indicated by the mow in the plot
(corresponding to 60 beats per minute). The maxima of this band with
respect to scale form a ridge. The locus of the ridge is shown as a black
curve on top of the band in FIG. 12(b). By employing a suitable resealing
of the scalogram, such as that given in equation (12), the ridges found
in wavelet space may be related to the instantaneous frequency of the
signal. In this way, the pulse rate is obtained from the PPG signal.
Instead of resealing the scalogram, a suitable predefined relationship
between the scale obtained from the ridge on the wavelet surface and the
actual pulse rate may also be used to determine the pulse rate.

[0171] By mapping the time-scale coordinates of the pulse ridge onto the
wavelet phase information gained through the wavelet transform,
individual pulses may be captured. In this way, both times between
individual pulses and the timing of components within each pulse may be
monitored and used to detect heart beat anomalies, measure arterial
system compliance, or perform any other suitable calculations or
diagnostics. Alternative definitions of a ridge may be employed.
Alternative relationships between the ridge and the pulse frequency of
occurrence may be employed.

[0172] As discussed above, pertinent repeating features in the signal give
rise to a time-scale band in wavelet space or a resealed wavelet space.
For a periodic signal, this band remains at a constant scale in the
time-scale plane. For many real signals, especially biological signals,
the band may be non-stationary, varying in scale, amplitude, or both over
time. FIG. 12(c) shows an illustrative schematic of a wavelet transform
of a signal containing two pertinent components leading to two bands in
the transform space in accordance with some embodiments. These bands are
labeled band A and band B on the three-dimensional schematic of the
wavelet surface. In some embodiments, the band ridge is defined as the
locus of the peak values of these bands with respect to scale. For
purposes of discussion, it may be assumed that band B contains the signal
information of interest. This will be referred to as the "primary band."
In addition, it may be assumed that the system from which the signal
originates, and from which the transform is subsequently derived,
exhibits some form of coupling between the signal components in band A
and band B. When noise or other erroneous features are present in the
signal with similar spectral characteristics of the features of band B
then the information within band B can become ambiguous (i.e., obscured,
fragmented or missing). In this case, the ridge of band A is followed in
wavelet space and extracted either as an amplitude signal or a scale
signal which will be referred to as the "ridge amplitude perturbation"
(RAP) signal and the "ridge scale perturbation" (RSP) signal,
respectively. The RAP and RSP signals may be extracted by projecting the
ridge onto the time-amplitude or time-scale planes, respectively. The top
plots of FIG. 12(d) show a schematic of the RAP and RSP signals
associated with ridge A in FIG. 12(c), Below these RAP and RSP signals
are schematics of a further wavelet decomposition of these newly derived
signals. This secondary wavelet decomposition allows for information in
the region of band B in FIG. 12(c) to be made available as band C and
band D. The ridges of bands C and D may serve as instantaneous time-scale
characteristic measures of the signal components causing bands C and D.
This technique, which will be referred to herein as secondary wavelet
feature decoupling (SWFD), allows information concerning the nature of
the signal components associated with the underlying physical process
causing the primary band B (FIG. 12(c)) to be extracted when band B
itself is obscured in the presence of noise or other erroneous signal
features.

[0173] In some instances, an inverse continuous wavelet transform may be
desired, such as when modifications to a scalogram (or modifications to
the coefficients of a transformed signal) have been made in order to, for
example, remove artifacts. In sonic embodiments, there is an inverse
continuous wavelet transform which allows the original signal to be
recovered from its wavelet transform by integrating over all scales and
locations, a and b:

where Cg is a scalar value known as the admissibility constant. It
is wavelet type dependent and may be calculated from:

C g = ∫ 0 ∞ | ψ ^ ( f ) | 2 f
f ( 18 ) ##EQU00018##

[0174] FIG. 12(e) is a flow chart of illustrative steps that may be taken
to perform an inverse continuous wavelet transform in accordance with the
above discussion. An approximation to the inverse transform may be made
by considering equation (16) to be a series of convolutions across
scales. It shall be understood that there is no complex conjugate here,
unlike for the cross correlations of the forward transform. As well as
integrating over all of a and b for each time t, this equation may also
take advantage of the convolution theorem which allows the inverse
wavelet transform to be executed using a series of multiplications. FIG.
12(f) is a flow chart of illustrative steps that may be taken to perform
an approximation of an inverse continuous wavelet transform. It will be
understood that any other suitable technique for performing an inverse
continuous wavelet transform may be used in accordance with the present
disclosure.

Transformed Respiration Modulation Ratios

[0175] A wavelet transform of physiological signals (e.g., PPG signals),
such as the continuous wavelet transform discussed in relation to FIGS.
12(a)-(f), may be used to generate a wavelet transform ratio surface for
further analysis of the respiration modulation components. FIG. 13 shows
an illustrative wavelet transform ratio surface 1300 of the physiological
signals of the type depicted in FIG. 4(a) in accordance with some
embodiments. Wavelet transform ratio surface 1300 is obtained by applying
a continuous wavelet transform to first and second physiological signals
corresponding to first and second wavelengths as discussed further in
relation to FIG. 14. In some embodiments, these signals correspond to the
red and infrared PPG signals used in traditional pulse oximetry, but the
methods described herein may be applied to other types of signals
corresponding to other wavelengths. A region of interest 1306 is defined
within lines 1302 and 1304 drawn across wavelet transform ratio surface
1300. Regions of interest are discussed further in relation to FIGS. 14
and 16.

[0176] In some embodiments, data representing a wavelet transform ratio
surface is stored in RAM or memory internal to processor 312 as any
suitable three-dimensional data structure such as a three-dimensional
array that represents the wavelet transform ratio surface as energy
levels in a time-scale plane. Any other suitable data structure may be
used to store data representing a wavelet transform ratio surface.

[0177] A transform technique may be used with a ratio of signal components
to determine the extent to which signal quality is degraded by motion
artifact. A determination of signal quality using a transform technique
may be used to confirm a determination of signal quality made using a
non-transform technique, such as the steps described in FIGS. 9-11. FIG.
14 is a flow chart 1400 of illustrative steps for using transforms and
ratios to analyze a physiological signal obtained from a subject in
accordance with some embodiments. FIG. 14 illustrates how to obtain first
and second physiological signals (step 1402), how to transform first and
second physiological signals to derive a ratio surface (steps 1404, 1406,
and 1408), and then how to identify and analyze a region of interest on
the ratio surface (steps 1410, 1412, and 1414). The steps of flow chart
1400 may be performed by processing equipment such as processor 316 of
FIG. 3, microprocessor 48 of FIG. 2, or any suitable processing device.
The steps of flow chart 1400, including the calculations associated with
the continuous wavelet transforms of the present disclosure as well as
the calculations associated with any suitable interrogations of the
transforms, may be performed by a digital processing device, or
implemented in analog hardware. It will be noted that the steps of flow
chart 1400 may be performed in any suitable order, and one or more steps
may be omitted entirely according to the context and application.

[0178] At step 1402, first and second physiological signals are obtained
from a subject. The first and second physiological signals may be red and
infrared PPG signals and may be obtained from any suitable source (e.g.,
sensor 12 of FIG. 2) using any suitable technique. A sensor from which a
signal is obtained may include any of the physiological sensors described
herein, or any other sensor. An obtained signal may be signal 402 as
shown in FIG. 4(a). An obtained signal may include multiple signals, for
example, in the form of a multi-dimensional vector signal or a
frequency-multiplexed or time-multiplexed signal. In some embodiments,
the physiological signal obtained at step 1402 includes two or more PPG
signals, which may be measured at two or more respective body sites of a
subject.

[0179] The physiological signal obtained at step 1402 may include first
and second physiological signals obtained as input signals. In some
embodiments, a first signal is a red PPG signal, and a second signal is
an infrared PPG signal. In some embodiments, each of the first and second
physiological signals includes a pulsatile component and a baseline
modulation component, such as pulsatile component 406 of FIG. 4(a) and
baseline modulation component 404 of FIG. 4(b). It will be noted that the
steps of flow diagram 1400 may be applied to any number of obtained
signals in accordance with the techniques described herein.

[0180] At step 1404, a first physiological signal, corresponding to a
first wavelength, is transformed to generate a first transformed signal.
In some embodiments, the transformation of step 1404 is applied to a
derivative of the first physiological signal. In some embodiments, the
transformation of step 1404 is a wavelet transform, such as a continuous
wavelet transform, as discussed above in relation to FIGS. 12(a)-(b). In
some embodiments, the first transformed signal calculated in step 1404 is
stored in ROM 52 or RAM 54 (FIG. 1). In some embodiments, the first
transformed signal calculated in step 1404 is processed further or
utilized immediately by processor 312 (FIG. 3) for determining
information about the subject's physiological condition.

[0181] At step 1406, a second physiological signal, corresponding to a
second wavelength, is transformed to generate a second transformed
signal. In some embodiments, the transform is applied to the derivative
of the second physiological signal, rather than the signal itself. The
second transformed signal may be calculated simultaneously with the first
transformed signal, or after the first transformed signal has been
calculated. In some embodiments, the transformation of step 1406 is a
wavelet transform, such as a continuous wavelet transform, as discussed
above in relation to FIGS. 12(a)-(b). In some embodiments, the
transformation of step 1406 is applied to time derivatives of the second
respiration signal component.

[0182] In some embodiments, the second transformed signal calculated in
step 1406 is stored in ROM 52 or RAM 54 (FIG. 1). In some embodiments,
the second transformed signal calculated in step 1406 is processed
further or utilized immediately by processor 312 (FIG. 3) for determining
information about the subject's physiological condition.

[0183] At step 1408, a ratio surface is derived from the first and second
transformed signals obtained at steps 1404 and 1406, respectively. In
some embodiments, the ratio surface is derived by dividing the first
transformed signal by the second transformed signal, or vice-versa. In
some embodiments, a ratio surface, such as the ratio surface 1300 shown
in FIG. 13, is derived by calculating a modulus of the first transformed
signal and a modulus of the second transformed signal and dividing the
first modulus by the second modulus. In embodiments where the transform
produces a complex signal, the modulus is defined as:

[0184] In some embodiments, deriving the ratio surface involves
normalizing the first and second physiological signals by a value. For
example, the respective magnitude of each of the first and second
physiological signals may be divided by the respective minimum, maximum,
mean, DC component, or standard deviation computed over a time window of
the first and second physiological signals.

[0185] At step 1410, a first region of interest on the ratio surface
derived in step 1408 is identified. In some embodiments, the first region
of interest, such as region of interest 1306 of FIG. 13, is related to a
respiration rate. In some embodiments, the first region of interest is an
area of the ratio surface having values close to an expected venous
saturation ratio value. In some embodiments, such an area of the ratio
surface is used as a confidence metric to improve existing respiration
rate detection by weighting the output of a respiration rate algorithm
according to the likelihood of the area being affected by motion.

[0186] At step 1412, a representative value is calculated for a first
region of interest. In some embodiments, calculating a representative
value involves filtering instantaneous values of the ratio surface. For
example, a median value over a specified time interval of a mean value
across the first region of interest identified at step 1410 may be
calculated, as discussed further in relation to FIG. 15. In some
embodiments, an estimated rate of respiration of the subject is
calculated based on an identified region of the ratio surface having
values close to an expected venous saturation ratio value. Such a
calculated respiration rate may be used in conjunction with other
methods, for example ridge tracking, to detect a baseline breathing band.

[0187] At step 1414, a determination is made based on the representative
value calculated at step 1412. In some embodiments, the determination is
whether the representative value calculated at step 1412 for the first
region of interest identified at step 1410 indicates respiration or
motion of the subject. In some embodiments, a representative value for
arterial oxygen saturation of the subject is obtained (e.g., in normal
oximetry fashion), and the representative value for arterial oxygen
saturation is compared to the representative value for the first region
of interest on the ratio surface. Similarity between the representative
value for arterial oxygen saturation of the subject and the
representative value for the first region of interest on the ratio
surface may be indicative of baseline modulations in the first and second
respiration signal components being due to respiration of the subject.

[0188] In some embodiments, determining whether the representative value
for the first region of interest indicates respiration or motion of the
subject involves identifying a second region of interest on the ratio
surface related to a cardiac pulse frequency. A representative value is
calculated for the second region of interest, and the representative
value for the first region of interest is compared with the
representative value for the second region of interest. The
representative values for the first and second regions of interest may
correspond to respective first and second functions. Comparing the
representative value for the first region of interest with the
representative value for the second region of interest may include, for
example, comparing corresponding points on the first and second
functions, respective median values of the first and second functions,
respective average values of the first and second functions, or
corresponding portions of the first and second functions. Similar
representative values for the first and second regions of interest that
are not near unity are indicative of baseline modulations in the first
and second signals being more likely caused by respiration than movement.

[0189] In some embodiments, a pulse oximetry system includes an indicator,
which may appear on display 28 of FIG. 1 or display 20 of FIG. 2, or any
other display that is communicatively coupled to the pulse oximetry
system, for indicating whether baseline modulation in at least one of the
first and second respiration signal components (e.g., respiration signal
components of red and IR wavelength components) is due to respiration or
motion of the subject. The indicator may indicate that baseline
modulation in at least one of the first and second respiration signal
components is due to motion if the representative value of the first
region of interest rises, as discussed further with respect to FIG. 15.
In some embodiments, the indicator includes a visible or audible alarm
that is triggered when a baseline modulation in at least one of the first
and second respiration signal components is due to motion of the subject.

[0190]FIG. 15 shows a plot 1500 with an illustrative representative value
1504 of the illustrative ratio surface 1300 of FIG. 13 in the
"respiration region" 1306 across time in accordance with some
embodiments. The instantaneous ratio value across the band defined by
lines 1302 and 1304 in FIG. 13 is shown by dashed line 1502 in plot 1500.
Representative value 1504, shown as a continuous line in plot 1500, is
the median value over a 20-second window of the mean value across the
band. Other methods of filtering may be used instead of or in addition to
this method of smoothing the instantaneous ratio value. The level of
representative value 1504 rises distinctly due to the subject's motion,
as indicated by the "Motion Region" label of plot 1500. In practice, such
a change in the level of a representative value is considered to be an
indication of motion artifact.

[0191] In some embodiments, multiple ridges are identified on and
extracted from a ratio surface to determine which ridge is most likely
due to respiration and which ridge is due to motion. The identification
and extraction of multiple ridges may be particularly useful for low
respiration rates which tend to have greater amplitude baseline signals
and are harder to differentiate from some forms of motion. In some
embodiments, identification and extraction of multiple ridges are used to
detect potential low rate breathing and to adjust filter characteristics
(e.g., cut-off ranges) in order to improve respiration rate calculation
accuracy. In some embodiments, the ridges are extracted using methods
discussed above in relation to FIGS. 12(c)-(d).

[0192]FIG. 16 is a flow chart 1600 of illustrative steps for analyzing a
ratio surface with more than one region of interest to determine signal
quality in accordance with some embodiments. FIG. 16 illustrates how to
calculate a representative value for a second region of interest of a
ratio surface (step 1602), and then how to calculate and use a short-term
difference to determine signal quality (steps 1604 and 1606). The
illustrative steps of flow chart 1600 may be performed as part of or in
addition to the illustrative steps of flow chart 1400. The steps of flow
chart 1600 may be performed by processing equipment such as processor 316
of FIG. 3, microprocessor 48 of FIG. 2, or any suitable processing
device. The steps of flow chart 1600 may be performed by a digital
processing device, or implemented in analog hardware. It will be noted
that the steps of flow chart 1600 may be performed in any suitable order,
and one or more steps may be omitted entirely according to the context
and application.

[0193] At step 1602, a representative value is calculated for a second
region of interest of a ratio surface related to a cardiac pulse
frequency. The ratio surface may be derived at step 1408 of flow chart
1400. The representative value for the second region of interest may be
calculated using the same method used at step 1412 to calculate the
representative value for a first region of interest, or a different
method may be used.

[0194] At step 1604, a short-term difference is calculated between the
representative value for the second region of interest and the
representative value for a first region of interest. The first region of
interest may be identified at step 1410 of flow chart 1400. The
representative value for the first region of interest may be calculated
at step 1412 of flow chart 1400.

[0195] At step 1606, the short-term difference calculated at step 1604 is
compared with a long-term difference between historical ratio surface
values near the cardiac pulse frequency and an expected respiration
frequency. Smaller deviations of the short-term difference from the
long-term difference may indicate that baseline modulations in the first
and second respiration signal components are due to respiration. Larger
deviations of the short-term difference from the long-term difference may
indicate that baseline modulations are due to motion.

[0196] In some embodiments, the long-term difference is a baseline finger
oxygen usage measure. The finger oxygen usage measure may be expected to
be relatively constant over time for baseline modulations that are due to
respiration, even as arterial SpO2 changes. A physiological signal, such
as a PPG signal, obtained at a subject's finger is useful for determining
whether a modulation in the signal is due to respiration or movement,
because the oxygen content of the arterial and venous blood at the finger
may be very similar due to oxygen demand at the finger tip being
relatively small. Any sudden deviations of a short-term difference from
the established finger oxygen usage measure may indicate that modulations
in the baseline region are due to the subject's motion. In other words, a
short term finger oxygen usage measure that is similar to the long term
average may indicate that recent modulations in the baseline region are
likely due to the subject's respiration.

Cardiac Output

[0197] A venous oxygen saturation value determined with sufficient
confidence (e.g., with adequate signal quality as determined by the steps
described with respect to any of FIG. 6, 9-11, 14, or 16) can be used
with a derived arterial oxygen saturation value to determine a patient's
cardiac output. This calculation can be done, for example, using a Fick
relationship. A representative Fick equation is:

Q = VO C aO 2 - C vO 2 ( 19 )
##EQU00019##

Q is cardiac output, quantized as a flow rate of blood, VO is an oxygen
consumption rate of a patient and may be quantized as units of oxygen per
unit time. CaO2 is concentration of oxygen in the arterial blood of
the patient, ideally correlated to the oxygen content of oxygenated blood
flowing from the heart. CvO2 is concentration of oxygen in venous
blood of the patient, ideally correlated to deoxygenated blood returning
to the heart after circulating through the body. The term
(CaO2-CvO2) represents a net oxygen concentration consumed by
the patient's body, and is also known as the arteriovenous oxygen
difference. It can be quantized as units of oxygen per unit volume. By
dividing the consumption rate by concentration, a flow rate can be
calculated, which corresponds to the cardiac output. Thus, given suitable
parameters, the Fick equation can be used to accurately determine the
cardiac output.

[0198] The Fick equation may be used to non-invasively determine cardiac
output, if the parameters of VO and (CaO2-CvO2) can be measured
non-invasively. In some embodiments, VO can be non-invasively measured by
a ventilator fitted to a patient. Non-invasive techniques, such as
photoplethysmography or any other suitable technique, may be used to
determine CaO2 and CvO2, to enable an accurate and fully
non-invasive method of determining cardiac output. The non-invasive
techniques provided in the present disclosure are advantageous over
conventional methods of measuring cardiac output, which require the
insertion of at least two catheters into a sensitive parts of a subject
to measure the oxygen content of arterial blood and venous blood. For
example, the catheter used to measure venous blood may be placed in the
vena cava, right atrium, right ventricle, or pulmonary artery. The
catheter used to measure arterial blood may be placed in the aorta or a
distal artery. Insertion of these catheters may be painful for the
subject and require extended preparatory and recovery time.

[0199] Non-invasive methods of measuring cardiac output, such as
rebreathing techniques (which estimate cardiac output via a modified Fick
equation from a respiratory regime where part of the time the patient
rebreathes carbon dioxide), transthoracic impedance, and bioreactance
measurements (which correlate resistance and/or reactance to cardiac
output), transthoracic Doppler ultrasound measurements (which compute the
velocity of blood over a major vessel of known area from which flow may
be computed), and pressure waveform analysis (which use non-invasively
measured pressure waveforms at the finger which are correlated via a
model to stroke volume and hence cardiac output) have been used in the
past, but are not as convenient as the non-invasive techniques using Fick
equations as discussed herein. The non-invasive techniques provided in
the present disclosure are faster and more comfortable for patients than
conventional invasive methods of measuring cardiac output, and may be
more accurate than some other non-invasive techniques.

[0200] FIG. 17 is a flow chart 1700 of illustrative steps for
non-invasively determining a cardiac output in accordance with some
embodiments. FIG. 17 illustrates how to measure a signal (step 1710), how
to determine arterial and venous blood oxygen content (steps 1720 and
1730), and how to determine cardiac output from the determined arterial
and venous blood oxygen content (step 1740). The steps of flow chart 1700
may be performed by processing equipment such as processor 316 of FIG. 3,
microprocessor 48 of FIG. 2, or any suitable processing device. The steps
of flow chart 1700 may be performed by a digital processing device, or
implemented in analog hardware. It will be noted that the steps of flow
chart 1700 may be performed in any suitable order, and one or more steps
may be omitted entirely according to the context and application.

[0201] In step 1710, a physiological signal is measured from a subject.
The physiological signal may be a PPG signal or any other suitable
signal. The physiological signal may include at least a first component
indicative of arterial blood oxygen content and a second component
indicative of venous return blood oxygen content. In some embodiments,
the first component and second component are differentiated and separated
in frequency, scale, or any other suitable indicator. For example,
modulation of a PPG signal corresponding to an arterial component occurs
at a higher frequency than modulation of a PPG signal corresponding to a
venous component. Arterial modulation may be observed as a high frequency
cardiac pulsatile component of a signal as shown in FIG. 4(a), in
contrast to a low frequency baseline as shown in FIG. 4(b).

[0202] In step 1720, an arterial blood oxygen content is determined based
at least in part on the first component indicative of arterial blood. The
first component indicative of arterial blood may be a high frequency
pulsatile component of a PPG signal comprising a red PPG signal and
infrared PPG signal. An arterial blood oxygen saturation value may be
determined by a ratio of ratios of the red PPG signal to the infrared PPG
signal. This blood oxygen saturation (SpO2), which may be expressed as a
percentage value, is used to determine the blood oxygen concentration by
multiplying the SpO2 by a concentration of hemoglobin (Hbconc) and
by a term representing the oxygen carrying capacity of the hemoglobin.
Hbconc may be quantized as units of mass per volume (g/mL). The
oxygen carrying capacity of hemoglobin is about 1.34 mL of oxygen volume
per gram of hemoglobin. Thus, the concentration of (bound) oxygen in the
blood at a given oxygen saturation SpO2 may be expressed as:

CO2=Hbconc*1.34*SpO2 mL O2/dL (20)

Hbconc may be assumed to be a nominal value based on patient
characteristics, measured by invasive means (such as a blood draw), or
from a non-invasive measurement.

[0203] In step 1730, a venous blood oxygen content is determined based at
least in part on the second component of the physiological signal
indicative of venous blood. The second component indicative of venous
blood may be a low frequency baseline component of a PPG signal
comprising a red PPG signal and infrared PPG signal. The low frequency
baseline component may be obtained by filtering the PPG signal around the
breathing rate of the subject. A venous blood oxygen saturation may be
determined from a ratio of ratios of the baseline red PPG and baseline
infrared PPG. The venous blood oxygen saturation may be converted to a
venous blood oxygen concentration using equation (20) described above,
replacing SpO2 with the estimate of SvO2.

[0204] In step 1740, a cardiac output of the subject is determined based
at least in part on the determined arterial blood oxygen content and
determined venous blood oxygen content. In some embodiments, the Fick
equation is used to determine the cardiac output using an arterial blood
oxygen concentration and a venous blood oxygen concentration, as
described in (19) above. If blood oxygen saturation values are measured
instead of blood oxygen concentrations, the Fick equation may be modified
to use blood oxygen saturation values as parameters.

Q = VO [ ( S aO 2 - S vO 2 ) * Hb conc
* 1.34 ] ( 21 ) ##EQU00020##

For example, in some embodiments, a PPG measurement device as shown in
FIG. 1 is connected to a subject. A PPG signal is measured from a signal
probe on the subject's body. The signal may be filtered to separate the
cardiac pulsatile component indicative of arterial blood, as shown in
FIG. 4(a), from the baseline component indicative of venous blood, as
shown in FIG. 4(b). Arterial blood oxygen saturation and venous blood
oxygen saturation are then determined by computing a ratio of ratios of
the red PPG and infrared PPG signals of the cardiac pulsatile and
baseline components. An oxygen consumption rate may be determined by
using a respirator, ventilator, or any other suitable measurement device
that may be part of the PPG monitoring system or separate from the PPG
monitoring system. The oxygen consumption rate and blood oxygen
saturation values may be input to modified Fick equation (21) to
calculate cardiac output.

[0205] The Fick equation calculates a flow rate by dividing a discharge
rate by a concentration. The flow rate corresponds to the cardiac output,
the discharge rate corresponds to the oxygen consumption rate, and the
concentration corresponds to the arteriovenous difference as described in
equation (19). Inaccuracies in the determination of the oxygen
consumption rate or arteriovenous difference affect the accuracy of the
determined cardiac output. In some embodiments, blood oxygen
concentration parameterized in a Fick relationship is derived based in
part on a PPG measurement. The PPG measurement analyzes the difference in
absorption of IR and red light by hemoglobin in a subject's blood.
Because the PPG measurement analyzes oxygen bound to hemoglobin, the PPG
measurement may not detect oxygen that is dissolved in the blood plasma.
The dissolved oxygen may be determined, given assumed or measured values
of partial pressures of dissolved oxygen content in arterial and venous
blood.

[0206] In some embodiments, a first physiological signal and a second
physiological signal are measured from different parts of a patient's
body to provide a stronger signal to noise ratio for arterial blood or
venous blood respectively. Determination of cardiac output using the Fick
method is most effective when the measure of arterial blood oxygen
content correlates to blood leaving the heart, and when the measure of
venous blood oxygen content correlates to blood entering the heart after
circulating through the entire body, also known as venous blood.

[0207] FIG. 18 is a flow chart 1800 of illustrative steps for
non-invasively determining a cardiac output using a first measured
physiological signal and a second measured physiological signal in
accordance with some embodiments. FIG. 18 illustrates how to measure a
first and second signal (steps 1810 and 1820), how to measure an oxygen
consumption rate (step 1830), how to determine arterial and venous blood
oxygen concentrations (steps 1840 and 1850), and then how to use the
arterial and venous blood oxygen concentrations to determine a cardiac
output (step 1860). The steps of flow chart 1800 may be performed as part
of or in addition to the steps of flow chart 1700. The steps of flow
chart 1800 may be performed by processing equipment such as processor 316
of FIG. 3, microprocessor 48 of FIG. 2, or any suitable processing
device. The steps of flow chart 1800 may be performed by a digital
processing device, or implemented in analog hardware. It will be noted
that the steps of flow chart 1800 may be performed in any suitable order,
and one or more steps may be omitted entirely according to the context
and application.

[0208] In step 1810, a first physiological signal indicative of arterial
blood is measured. In some embodiments, arterial blood oxygen content is
measured using a PPG probe at the forehead, finger, chest, or any other
suitable site, assuming that there is a negligible drop in blood oxygen
saturation in the arterial blood en route to the peripheries.

[0209] In step 1820, a second physiological signal indicative of venous
blood is measured. The second physiological signal may be measured using
the same probe used to measure the first physiological signal, or
measured using a different probe. The measurement of the second signal
may be at the same site as the measurement of the first physiological
signal, or at a second site different from the first.

[0210] The measurement of venous blood oxygen content is more constrained
compared to the measurement of arterial blood oxygen content. Firstly,
the venous measurement should be indicative of venous blood, which is
representative of oxygen consumed during circulation of blood from the
heart, through the body, and back to the heart. If venous return blood is
not measured, then the determination of cardiac output may be inaccurate,
at least because, depending on the measurement site chosen and the local
blood flow relative to the local tissue's oxygen demand, a higher (or
lower) SvO2 would be measured, leading to a lower (or higher)
arteriovenous venous oxygen difference (CaO2-CvO2) and
therefore a higher (or lower) than expected cardiac output. Secondly,
measurement of venous blood by photoplethysmography is difficult because
veins are usually located deep underneath the skin of a subject's body.
In order to detect the blood in the veins, specialized probes having high
sensitivity or measurement sites with veins close to the surface are
required. For example, a PPG probe placed through the mouth into the
esophagus near the chest cavity of a subject would provide a good measure
of venous blood.

[0211] In step 1830, an oxygen consumption rate is measured. In some
embodiments, the oxygen consumption rate is measured using a ventilator,
respirator, or any other suitable measurement device. The measurement
device for oxygen consumption may be part of a patient monitoring device,
or may be a separate device that provides data that may be manually or
automatically input into the patient monitoring device.

[0212] In step 1840, an arterial blood oxygen concentration based on the
first physiological signal is determined. In some embodiments, this
arterial blood oxygen concentration is determined from a blood oxygen
saturation derived from a PPG signal. For example, a first component
indicative of arterial blood may be a high frequency pulsatile component
of a PPG signal comprising a red PPG signal and infrared PPG signal. An
arterial blood oxygen saturation value may be determined from a ratio of
ratios of the red PPG signal to the infrared PPG signal.

[0213] In step 1850, a venous blood oxygen concentration based on the
second physiological signal is determined. In some embodiments, the
venous blood oxygen concentration is determined from a blood oxygen
saturation derived from a PPG signal. For example, a second component
indicative of venous blood may be a baseline component of a PPG signal
comprising a red PPG signal and infrared PPG signal. A venous blood
oxygen saturation value may be determined from a ratio of ratios of the
red PPG signal to the infrared PPG signal.

[0214] In step 1860, cardiac output is determined by using a Fick
equation. In some embodiments, blood oxygen saturation values are
measured and input into a modified Fick equation, described by equation
(21), to determine the cardiac output.

[0215] To more accurately determine a cardiac output using Fick's
equation, the Fick equation may be modified to account for dissolved
oxygen by adding a term indicative of dissolved oxygen. Indicators of
dissolved oxygen may include partial pressure, spectral absorbance, or
any other suitable indicator. In some embodiments, the blood oxygen
content may be modified by adding a term indicative of partial pressure.
According to the ideal gas law, provided below in equation (22), pressure
directly correlates with the number of moles of oxygen. P is pressure, V
is volume, n is a number of moles, R is an ideal gas constant, and T is a
temperature:

PV=nRT (22)

By dividing both sides by volume, the pressure P is directly proportional
to a molar concentration (n/V). Ideal gas analysis applies to a pure
gaseous phase, but also correlates to dissolved gases within a solution.
Ideal gas analysis relates partial pressure to dissolved gas content.

P = n V RT ( 23 ) ##EQU00021##

As an example, the Fick Equation may be modified by adding a partial
pressure term indicative of dissolved gases:

The partial pressure term is (PaO2-PvO2)*K, where K is a
constant to convert from pressure to concentration and may account for
temperature, fluid properties, or any other suitable environmental
factors. In practice the value of -0.003 is often used for the constant
K.

[0216] FIG. 19 is a flow chart 1900 of illustrative steps for
non-invasively determining a cardiac output and correcting for dissolved
gases in accordance with some embodiments. FIG. 19 illustrates how to
measure a first and second signal (steps 1910 and 1920), how to measure
an oxygen consumption rate (step 1930), how to determine arterial and
venous blood oxygen concentrations (steps 1940 and 1950), and then how to
correct for dissolved gases to determine a cardiac output (steps 1960,
1970, 1980, and 1990). The steps of flow chart 1900 may be performed as
part of or in addition to the steps of flow chart 1700 or 1800. The steps
of flow chart 1900 may be performed by processing equipment such as
processor 316 of FIG. 3, microprocessor 48 of FIG. 2, or any suitable
processing device. The steps of flow chart 1900 may be performed by a
digital processing device or implemented in analog hardware. It will be
noted that the steps of flow chart 1900 may be performed in any suitable
order, and one or more steps may be omitted entirely according to the
context and application.

[0217] In step 1910, a first physiological signal indicative of arterial
blood is measured. The first physiological signal may be a PPG signal or
any other suitable signal. In some embodiments, the first physiological
signal may be a PPG signal comprising a red PPG signal component and
infrared PPG signal component. For example, the PPG signal components may
correspond to a high frequency cardiac pulsatile component indicative of
arterial blood, as illustrated in FIG. 4(a).

[0218] In step 1920, a second physiological signal indicative of venous
return blood is measured. The second physiological signal may be a PPG
signal or any other suitable signal. In some embodiments, the second
physiological signal is a PPG signal comprising a red PPG signal
component and infrared PPG signal component. For example, the PPG signal
components may correspond to a low frequency baseline component
indicative of venous blood, as illustrated in FIG. 4(b). The second
physiological signal may be measured using the same probe used to measure
the first signal, or a second probe different from the first probe. The
second signal may be measured at the same site on the patient as the
first probe, or at a second site. In some embodiments, the second
physiological signal is measured at a site indicative of venous blood
return. For example, the second physiological signal may be a signal
measured from a PPG probe placed in the mouth through the esophagus into
the chest cavity of the patient.

[0219] In step 1930, an oxygen consumption rate is measured. The oxygen
consumption rate may be measured by a ventilator, respirator, or any
other suitable measurement device.

[0220] In step 1940, arterial blood oxygen concentration is determined
based in part on the first physiological signal. In some embodiments, the
blood oxygen concentration is determined from a blood oxygen saturation
derived from a PPG signal. For example, equation (20) may be used to
relate the blood oxygen saturation to the blood oxygen concentration.

[0221] In step 1950, venous blood oxygen concentration is determined based
in part on the second physiological signal. In some embodiments, the
blood oxygen concentration is determined from a blood oxygen saturation
derived from a PPG signal. For example, equation (20) may be used to
relate the blood oxygen saturation to the blood oxygen concentration.

[0222] In step 1960, a determination is made whether to correct for
dissolved gases. This determination may be made by processor 312 in FIG.
3, microprocessor 48 in FIG. 2, or any other suitable processing
equipment. The determination may be made in response to user inputs 56 or
stored settings in ROM 52 or RAM 54 in FIG. 2, or any other suitable
storage equipment. If there is a determination to correct for dissolved
gases, the next step will be step 1970, determination of the cardiac
output using the modified Fick equation. If there is a determination not
to correct for dissolved gases, the next step will be step 1980,
determination of cardiac output using the unmodified Fick equation.

[0223] The above described embodiments of the present disclosure are
presented for purposes of illustration and not of limitation, and the
present disclosure is limited only by the claims which follow.

Patent applications by James Watson, Dunfermline GB

Patent applications by Paul Addison, Edinburgh GB

Patent applications by Nellcor Puritan Bennett Ireland

Patent applications in class And other cardiovascular parameters

Patent applications in all subclasses And other cardiovascular parameters